research paper about rice production in the philippines

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• Published: 14 October 2022

Two decades of rice research in Indonesia and the Philippines: A systematic review and research agenda for the social sciences

• Ginbert P. Cuaton   ORCID: orcid.org/0000-0002-5902-3173 1 &
• Laurence L. Delina   ORCID: orcid.org/0000-0001-8637-4609 1  

Humanities and Social Sciences Communications volume  9 , Article number:  372 ( 2022 ) Cite this article

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• Development studies
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While rice studies are abundant, they usually focus on macro-level rice production and yield data, genetic diversity, cultivar varieties, and agrotechnological innovations. Moreover, many of these studies are either region-wide or concentrated on countries in the Global North. Collecting, synthesizing, and analyzing the different themes and topic areas in rice research since the beginning of the 21st century, especially in the Global South, remain unaddressed areas. This study contributes to filling these research lacunae by systematically reviewing 2243 rice-related articles cumulatively written by more than 6000 authors and published in over 900 scientific journals. Using the PRISMA 2020 guidelines, this study screened and retrieved articles published from 2001 to 2021 on the various topics and questions surrounding rice research in Indonesia and the Philippines—two rice-producing and -consuming, as well as emerging economies in Southeast Asia. Using a combination of bibliometrics and quantitative content analysis, this paper discusses the productive, relevant, and influential rice scholars; key institutions, including affiliations, countries, and funders; important articles and journals; and knowledge hotspots in these two countries. It also discusses the contributions of the social sciences, highlights key gaps, and provides a research agenda across six interdisciplinary areas for future studies. This paper mainly argues that an interdisciplinary and comparative inquiry of potentially novel topic areas and research questions could deepen and widen scholarly interests beyond conventional natural science-informed rice research in Indonesia and the Philippines. Finally, this paper serves other researchers in their review of other crops in broader global agriculture.

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Introduction.

Rice feeds the majority of the world’s population and employs millions, especially in developing countries in the Global South (Muthayya et al., 2014 ). Rice consumption has increased globally over the last decade. Statista data show that, in the cropping year 2020/2021, the world population consumed about 504.3 million metric tons of rice, increasing from 437.18 million metric tons in 2008/2009 (Shabandeh, 2021 ). These data highlight the crop’s global contribution and importance, especially in realizing the Sustainable Development Goals (SDGs), the blueprint for global prosperity (Gil et al., 2019 ). The SDGs call for systems transformation, including in agriculture, guided by the principles of sustainability and equity, driven by the leave-no-one-behind aphorism, to address the root causes of perennial poverty and chronic hunger.

Pathologist M. B. Waite ( 1915 ) pointed out that the apparent indicator of progress in modern agriculture is the application of scientific research and the subsequent modification and improvement of farming systems based on those research. For example, the Green Revolution resulted in increased agricultural production in developing countries due to the transfer of agrotechnological innovations from countries in the Global North to countries in the Global South. Although, we acknowledge that this project came with a cost (Glaeser, 2010 ; Pielke and Linnér, 2019 ; Pingali, 2012 ).

Regional rice studies have proliferated in Europe (Ferrero and Nguyen, 2004 ; Kraehmer et al., 2017 ), the Americas (Singh et al., 2017 ), Africa (Zenna et al., 2017 ), the Asia Pacific (Papademetriou et al., 2000 ), and South Asia (John and Fielding, 2014 ). Country studies on rice production have also emerged in Australia (Bajwa and Chauhan, 2017 ), China (Peng et al., 2009 ), and India (Mahajan et al., 2017 ). Scholars have also systematically reviewed rice’s phytochemical and therapeutic potentials (Sen et al., 2020 ), quality improvements (Prom-u-thai and Rerkasem, 2020 ), and its role in alleviating the effects of chronic diseases and malnutrition (Dipti et al., 2012 ).

These extant studies, however, are limited on at least three fronts. First, their foci were on rice production, yield, and operational practices and challenges at the macro level. Second, there have been zero attempts at synthesizing this corpus since the 21st century. Third, there are also no attempts at examining the various rice research areas that scholars, institutions, and countries need to focus on, especially in developing country contexts, and their nexuses with the social sciences. This paper addresses these gaps by unpacking and synthesizing multiple rice studies conducted in the emerging Southeast Asian economies of Indonesia and the Philippines from 2001 to 2021. A focus on these developing countries matters since they are home to over 35 million rice farmers (IRRI, 2013 ).

We conducted our review from the Scopus database, using a combination of bibliometric and quantitative content analyses. Section “Results and discussions” reports our results, where we discuss (1) the most relevant and influential rice scholars and their collaboration networks; (2) the most rice research productive institutions, including author affiliations, their countries, and their research funders; and (3) the most significant articles and journals in rice research. This section also identifies 11 topic areas belonging to four major themes of importance for rice research in the two countries. Section “Contributions from and research agenda for the social sciences” provides a research agenda, where we identify and discuss the contributions of our review in terms of future work. Despite the preponderance of rice research in the last two decades and more in Indonesia and the Philippines, contributions from the social sciences remain marginal. Thus, in the section “Conclusion”, we conclude that emphasis is needed on expanding and maximizing the contributions of social scientists given the many opportunities available, especially for conducting interdisciplinary and comparative rice research in these Southeast Asian countries.

Review methods and analytical approach

We used bibliometric and quantitative content analyses to systematically categorize and analyze more than two decades of academic literature on rice in Indonesia and the Philippines. Bibliometric methods, also known as bibliometrics, have grown to be influential in evaluating various research fields and topic areas. Bibliometrics mushroomed because of the increasing availability of online databases and new or improved analysis software (Dominko and Verbič, 2019 ). Bibliometrics quantitatively and statistically analyze research articles using their bibliographic data, such as authors, affiliations, funders, abstracts, titles, and keywords. These data are analyzed to identify and assess the development, maturity, research hotspots, knowledge gaps, and research trends (Aria and Cuccurullo, 2017 ). For example, bibliometrics have been used in reviewing hydrological modeling methods (Addor and Melsen, 2019 ), business and public administration (Cuccurullo et al., 2016 ), and animals’ cognition and behavior (Aria et al., 2021 ).

This review article used bibliometrix , a machine-assisted program that offers multiple options and flexibility to map the literature comprehensively (Aria and Cuccurullo, 2017 ). We run this program using R Studio version 4.1.2 (2021-11-01; “Bird Hippie”) for its source code readability, understandability, and easy-to-do computer programming (Cuaton et al., 2021 ). We used bibliometrix in three critical analytical phases: (a) importing and converting data to R format, (b) identifying our dataset’s collaboration networks and intellectual and conceptual structures, and (c) processing, presenting, and analyzing our dataset. Bibliometrix, however, is unable to produce specific data that we want to highlight in this paper; examples of these are our coding criteria on interdisciplinarity and author gender, where such information was not captured in the articles’ bibliographic data in Scopus. We addressed these issues by conducting a quantitative content analysis (QCA) of our dataset. QCA is a method to record, categorize, and analyze textual, visual, or aural materials (Coe and Scacco, 2017 ). QCA has been applied in other reviews, such as in energy research development in the social sciences (Sovacool, 2014 ), the concepts of energy justice (Jenkins et al., 2021 ), and in examining agricultural issues in Botswana (Oladele and Boago, 2011 ) and Bangladesh (Khatun et al., 2021 ).

Search strategies

We constructed our dataset from the Scopus database, which we accessed via our institution’s online library on 14 November 2021. Scopus is a scientific database established in 2004 and owned by Elsevier Ltd. (Elsevier, 2021 ). We excluded other databases, such as Google Scholar, ScienceDirect, Web of Science, and EBSCO, suggesting one potential bias in our review (Waltman, 2016 ; Zupic and Čater, 2015 ). Our decision to exclusively use Scopus arises from two main reasons. First, the database has broader coverage than others, including the abovementioned (Falagas et al., 2008 ). Scopus includes new and emerging journals published in developing countries like Indonesia and the Philippines, our focus countries. Second, Scopus has a user-friendly interface and its search options allow researchers to flexibly explore its universe of indexed articles based on authors, institutions, titles, abstracts, keywords, and references (Donthu et al., 2021 ).

We followed the PRISMA 2020 Guideline (Preferred Reporting Items for Systematic reviews and Meta-Analyses) (Page et al., 2021 ) in our search for potential rice-related studies in Indonesia and the Philippines (see Fig. 1 ). We used the initial search string: “rice” AND “Indonesia*” OR “Philippine*” (asterisk or “*” was used as a wildcard search strategy) and limited the year coverage from 2001 to 2021. Our first round of searches resulted in 3846 documents (results as of 14 November 2021). We filtered these documents by including only peer-reviewed, full-text English articles on rice. We did not include any documents from the grey literature (e.g., news items, press releases, government or corporate reports), and other document types indexed in Scopus such as reviews, books, conference papers, errata, comments, editorials, and short reports.

Our initial result of 3846 documents (results as of 14 November 2021) was filtered by including only peer-reviewed, full-text English articles on rice, resulting in 2243 eligible documents.

We also excluded articles with irrelevant keywords by using the following combined queries:

This resulted in 2243 eligible documents. We downloaded these documents as raw files in BibTex format and imported them to Biblioshiny , a web interface in Bibliometrix, where they were further filtered. Our verified final dataset comprises 2243 full-text English articles cumulatively written by 6893 authors and published across 909 journals (see Table 1 ).

Structure and analytical approach

We examined the authors’ profiles based on their gender, relevance in the study, and global impact. For gender, we coded them into ‘man,’ ‘woman,’ and ‘undetermined’ because some did not put enough information that helps in gender identification. We identified their gender by counter-checking their Scopus profiles to their verified accounts in Google Scholar, ResearchGate, Publons/Web of Science, or institutional profiles. We measured the authors’ relevance and impact against their (a) productivity, (b) citations, and (c) H-indices. We acknowledge, however, that some Filipino and Indonesian scholars, whose papers may not be indexed in Scopus, could also be prolific based on different parameters, but we excluded them. We proceeded to map the collaboration networks of these authors to identify “who works with whom on what.” A collaboration network illustrates nodes (circle shape) as authors and links (connecting lines) as co-authorships (Glänzel and Schubert, 2005 ).

Institutions, countries, funders

Following Sovacool ( 2014 ), we categorized the authors’ institutions into four: (1) University and research included authors who are researchers, instructors/lecturers/professors, other academic faculty from various non-university research think tanks, institutes, and national and local research centers; (2) Government consisted country or state departments, bureaus, ministries, and other government regulatory bodies; (3) Interest groups and NGOs included intergovernmental bodies, such as the United Nations Food and Agriculture Office (FAO) and international organizations like the International Rice Research Institute (IRRI) and Oxfam; and (4) Banking and finance encompassed players from the finance sector, including multilateral development banks such as the Asian Development Bank (ADB), World Bank, and the International Fund for Agricultural Development (IFAD). After coding and categorizing, we analyzed the authors’ institutional collaboration networks.

We identified the country’s productivity and coded them by global region based on their geographical location: (a) Asia, (b) Australia, New Zealand, and South Pacific, (c) Europe, (d) North America, (e) South America, and (f) Africa. We did this to show how various countries have been researching rice in Indonesia and the Philippines since the 21st century.

We then constructed a country collaboration map as a visual macro-representation of countries working together on rice research using these data. Bibliometrix, however, measured the country’s productivity based on the corresponding authors’ affiliations. We, therefore, noted two critical points here. First, many corresponding authors may have multiple institutional affiliations. For example, one corresponding author may belong to more than two affiliations (e.g., a corresponding Filipino author may have concurrent institutional affiliations in Japan, Australia, and New Zealand). Second, the corresponding authors may not necessarily be nationals of that country. Note that the unit of analysis is based on the corresponding authors’ institutional affiliations at the time of publication and not on their country/ies of citizenship or nationality. Despite these, our findings still provide insight into the macro-level productivity of countries conducting rice research in Indonesia and the Philippines.

We analyzed the funders using Scopus’ in-house Analytics Tool and determined their relevance based on the number of articles mentioning them in the Funding source or Acknowledgment section in the paper. We categorized the funders into six: (1) government (e.g., ministries, departments, or regulatory agencies), (2) research (e.g., research councils, research centers, and national academies), (3) foundations and non-government organizations (NGOs), (4) universities, (5) private companies and corporations, and (6) intergovernmental organizations/IGOs, including multilateral development banks.

Articles and journals

In terms of interdisciplinarity, we coded the articles as (a) interdisciplinary, (b) disciplinary, or (c) unidentified by using the authors’ department or division affiliation/s as a proxy to determine their disciplinary training. We coded an article as interdisciplinary if it belonged to any of the three criteria: (1) it had an author that had training or belonged to a department/division in at least two conventional disciplines (e.g., agriculture, anthropology, sociology, biology); (2) it had an author that had a self-identified interdisciplinary department (e.g., interdisciplinary division, sustainability, agriculture economics, etc.); or (3) it had at least two authors with different disciplinary training or expertise (e.g., business and economics; crop science and political science, etc.). We coded an article as disciplinary if its author/s had only belonged to one conventional department/division affiliation (e.g., Division of Agriculture, Department of Economics, Division of Environmental Science, etc.). On the other hand, we coded an article as undetermined when the authors had only indicated the name of their institutions or did not indicate their departmental or division affiliations (e.g., only the University of the Philippines, IRRI, Universitas Gadja Mada, etc.).

We examined the articles based on their local relevance and global influence. Bibliometrix measured the articles’ relevance based on their “local citations” or citations received from the 2243 articles of our sample dataset. We did this to determine which papers are considered relevant by authors studying various areas of rice research in Indonesia and the Philippines. Global influence is measured based on the articles’ citations from the global research community or other scientific works beyond our sample dataset. We also conducted a co-citation analysis of the cited references. Co-citation is the frequency by which articles cite together two or more articles relevant to the topic areas of inquiry (Aria and Cuccurullo, 2017 ). Bibliometrix had identified some co-cited articles published before our timeline of interest (i.e., pre-2001) which provide scholars with a more profound understanding of rice research in the two countries.

On the other hand, Bibliometrix identified the most relevant journals based on the number of papers the journals had published and the local citations of the articles. These data guide readers and researchers on which journals to look for on rice studies in Indonesia and the Philippines.

Knowledge hotspots

Bibliometrix creates a thematic map that allows researchers to identify which study areas have been adequately explored and which areas need further investigation or re-investigation to identify knowledge hotspots and research gaps (Aria and Cuccurullo, 2017 ). Della Corte et al. ( 2019 , pp. 5–6) discussed the major themes in Bibliometrix in the following:

“Themes in the lower-right quadrant are the Basic Themes , characterized by high centrality and low density. These themes are considered essential for a research field and concerned with general topics across different research areas.

Themes in the upper-right quadrant are the Motor Themes , characterized by high centrality and density. Motor themes are considered developed and essential for the research field.

Themes in the upper-left quadrant are the highly developed and isolated themes or Niche Themes . They have well-developed internal links (high density) but unimportant external links, which could be interpreted as having limited importance for the field (low centrality).

Themes in the lower-left quadrant are known as Emerging or Declining Themes . They have low centrality and density, making them weakly developed and marginal.”

Contributions from and research agenda for the social sciences

As interdisciplinary environmental and social scientists, we also focused our review on the social studies of rice in the two countries. This section highlighted the gaps between the natural and the social sciences in rice research and advanced a research agenda for interdisciplinary and comparative social scientists.

Limitations

As in any systematic review, we acknowledge certain limitations to our work. We discuss four of these.

First, to keep a certain level of reliability, we focused only on peer-reviewed full-length research articles written in the English language and indexed in the Scopus database. Therefore, we may have excluded some relevant articles, including those written in Filipino, Indonesian, and other local or indigenous languages and published in local or international journals but are not indexed in Scopus. Our review also excluded conference papers, commentaries, book reviews, book chapters, conference reviews, data papers, errata, letters, notes, and non-academic publications like policy briefings, reports, and white papers.

Second, in our quantitative content analysis, we acknowledge the highly cis-heteronormative approach we used in coding the author’s gender as “man” or “woman.” We identified these genders from the names and pictures of the authors in their verified Scopus, Publons/ Web of Science, and institutional profiles. It is not our deliberate intention to neglect the varying genders of researchers and scientists beyond the traditional binary of man or woman.

Third, we recognize that our analysis cannot directly identify how much each funder provided as the unit of analysis in bibliometrix may depend on how prolific researchers were in publishing articles despite smaller funds. For instance, one research project supported by Funder A with US$1 million may have published only one article based on their project design or the funder's requirement. Since the authors published only one paper from this project, the data could show that Funder A only funded one research. Another research project, supported by Funder B, with only US$300,000 in funding, may have published more than five papers; therefore, more articles counted as funded by Funder B. This issue is not within the scope of our review.

Lastly, it should be noted that the future research works we discussed were highly influenced by our research interests and the general overview of the literature, and thus neither intend to cover nor aim to discuss the entire research topics that other scholars could study.

Despite these limitations, we strongly argue that our review provided relevant insights and proposed potentially novel topic areas and research questions for other scholars to explore, especially social scientists, in deepening and widening rice research in Indonesia and the Philippines. To end, we hope that researchers heed our call to conduct more interdisciplinary and comparative rice-related studies in these two emerging Southeast Asian countries.

Results and discussions

Our dataset comprises 2243 peer-reviewed journal articles cumulatively written by 6893 authors who cited around 80,000 cumulative references. The average annual publications from 2001 to 2013 were only 57 papers but elevated to hundreds beginning in 2014 (Fig. 2 ).

The average number of annual publications on rice research in Indonesia and the Philippines from 2001 to 2013 was only 57 papers but elevated to hundreds beginning in 2014.

Of the 159 authors, one had a duplicate profile; thus, we identified 158 authors publishing on rice studies; the majority (66%) are men. The top 50 most prolific scholars produced a little over 25% (567 articles) of the total articles. Australian ecologist Finbarr Horgan topped this list ( n  = 21), followed by Bas Bouman and Grant Singleton—each with 20 articles. The top 10 authors with the highest number of publications have affiliations with the IRRI, the University of the Philippines, the University of Gadjah Mada, and the Philippine Rice Research Institute (PhilRice). For the full list of prolific scholars with at least 10 articles published, see Supplementary Table 1 .

In terms of the authors with the most local citations, although Finbarr Horgan has the most documents, Johan Iskandar ( n  = 36 citations) from the Universitas Padjadjaran, who studies rice genetic diversity, is the most cited. Local citations refer to the citations received by authors from our sample dataset of 2243 articles. Muhidin Muhidin from the Universitas Halu Oleo and Ruhyat Partasasmita from the Universitas Padjadjaran, followed him with 30 and 28 local citations, respectively. Common to these three authors are their Biology background/expertise and interest in rice genetic diversity. To check the top 20 most locally cited scholars, refer to Supplementary Table 2 .

The H-index is the author-level measure of publications’ productivity and citation impacts (Hirsch, 2005 ). Bas Bouman (H-index = 18) leads the top 10 scholars among rice-related researchers in Indonesia and the Philippines. Yoshimichi Fukuta (H index = 13) and Shaobing Peng (H index = 13) followed him. These three authors are affiliated with or have collaborated with the IRRI. To check the top 10 scholars with the highest H-indices, refer to Supplementary Table 3 .

Figure 3 reveals the top 80 authors who collaborate across eight major clusters of rice research. The Red cluster shows Finbarr Horgan as the most prominent author with at least four significant collaborators in pest management, specifically on rice stemborers (Horgan et al., 2021 ), anthropods’ biodiversity in tropical rice ecosystems (Horgan et al., 2019 ), and virulence adaptations of rice leafhoppers (Horgan et al., 2018 ). In the Purple Cluster, Yoshimichi Fukuta has multiple publications with at least six collaborators in the study of rice blast (Ebitani et al., 2011 ; Kadeawi et al., 2021 ; Mizobuchi et al., 2014 ). In the Brown cluster, Bernard Canapi from the IRRI has collaborated with at least five scholars in the study of rice insect pest management (Cabasan et al., 2019 ; Halwart et al., 2014 ; Litsinger et al., 2011 ), farmers’ preference for rice traits (Laborte et al., 2015 ), and the drivers and consequences of genetic erosion in traditional rice agroecosystems in the Philippines (Zapico et al., 2020 ). The Gray cluster shows that Siti Herlinda has collaborated with at least four scholars to study anthropods in freshwater swamp rice fields (Hanif et al., 2020 ; Herlinda et al., 2020 ) and the benefits of biochar on rice growth and yield (Lakitan et al., 2018 ).

The authors’ collaboration networks show eight major clusters of rice research in Indonesia and the Philippines.

Institutions

Author affiliations.

In terms of institutional types, Fig. 4 shows that most rice researchers in Indonesia and the Philippines have affiliations with “University and research.” Figure 5 shows the top 20 institutions in terms of research productivity led by the IRRI, the University of the Philippines System, the PhilRice, the Institute Pertanian Bogor/IPB University, and the University of Gadja Mada. These 20 institutions produced 66% of the articles in our dataset.

The majority of rice researchers in Indonesia and the Philippines have affiliations with “University and research”.

The top 5 most productive institutions in terms of rice research in Indonesia and the Philippines are the IRRI, the University of the Philippines System, the PhilRice, the Institute Pertanian Bogor/IPB University, and the University of Gadja Mada.

Scholars affiliated with the IRRI have written the most papers (at least 19% or 358 articles) in our dataset. The range of topics covers both regional and country studies. Some regional examples include the drivers of consumer demand for packaged rice and rice fragrance in South and Southeast Asia (Bairagi et al., 2020 ; Bairagi, Gustafson et al., 2021 ). Country studies, for example, include an investigation of rice farming in Central Java, Indonesia (Connor et al., 2021 ), the cultural significance of heirloom rice in Ifugao in the Philippines (Bairagi, Custodio et al., 2021 ), and the distributional impacts of the 2019 Philippine rice tariffication policy (Balié and Valera, 2020 ).

The University of the Philippines System, with rice scholars affiliated with their campuses in Los Baños, Diliman, Mindanao, and Manila, produced the next largest number of papers (more than 200 or 10%) on topics about rice pests and parasites (Horgan et al., 2019 , 2021 ; Vu et al., 2018 ), weed control (Awan et al., 2014 , 2015 ; Fabro and Varca, 2012 ), and climate change impacts on rice farming (Alejo and Ella, 2019 ; Ducusin et al., 2019 ; Gata et al., 2020 ). Social studies of rice conducted by the University of the Philippines researchers include indigenous knowledge on climate risk management (Ruzol et al., 2020 , 2021 ), management options in extreme weather events (Lopez and Mendoza, 2004 ), agroecosystem change (Aguilar et al., 2021 ; Neyra-Cabatac et al., 2012 ), and the development and change over time of rice production landscapes (Santiago and Buot, 2018 ; Tekken et al., 2017 ).

PhilRice, a government-owned corporation under the Department of Agriculture (Official Gazette of the Philippines, 2021 ), is the third most prolific rice research-producing institution (122 papers) on topics ranging from nematodes or rice worms (Gergon et al., 2001 , 2002 ) and arthropods (invertebrates found in rice paddies) (Dominik et al., 2018 ), hybrid rice (Perez et al., 2008 ; Xu et al., 2002 ), alternate wetting-and-drying technology (Lampayan et al., 2015 ; Palis et al., 2017 ), and community development strategies on rice productions (Romanillos et al., 2016 ).

The IPB University, a public agrarian university in Bogor, Indonesia, investigates rice productivity and sustainability (Arif et al., 2012 ; Mucharam et al., 2020 ; Setiawan et al., 2013 ), irrigation (Nugroho et al., 2018 ; Panuju et al., 2013 ), extreme weather events such as drought (Dulbari et al., 2021 ), floods (Wakabayashi et al., 2021 ), and emerging social issues such as food security (Putra et al., 2020 ), land-use change (Chrisendo et al., 2020 ; Munajati et al., 2021 ), and sustainability (Mizuno et al., 2013 ). This university has 23 research centers, including those which focus on environmental research; agricultural and village development; engineering applications in tropical agriculture; Southeast Asian food and agriculture; and agrarian studies.

Universitas Gadja Maja in Yogyakarta, Indonesia, hosts 21 research centers, including its Agrotechnology Innovation Centre. It carries out research incubation and development activities, product commercialization, and integration of agriculture, animal husbandry, energy, and natural resources into a sustainable Science Techno Park. Some of their published studies focused on drought-tolerant rice cultivars (Salsinha et al., 2020 , 2021 ; Trijatmiko et al., 2014 ), farmers’ technical efficiency (Mulyani et al., 2020 ; Widyantari et al., 2018 , 2019 ), systems of rice intensification (Arif et al., 2015 ; Syahrawati et al., 2018 ), and climate change adaptation (Ansari et al., 2021 ).

In terms of institutional collaboration, the IRRI tops the list with at least eleven collaborators (Fig. 6 ), including the Japan International Center for Agricultural Sciences, the PhilRice, the University of the Philippines System, and the Indonesian Center for Rice Research.

The IRRI, as an international organization focused on many aspects of rice, is not surprising to have the greatest number of institutional collaborators ( n  = 11 institutions).

Rice studies’ authors are from at least 79 countries; the majority of them are working in Asia (79%), followed by Europe (13%) and North America (9%). At least 90% of rice scholars are in Indonesia, and more than 51% have affiliations in the Philippines, followed by Japan, the USA, and China. For the list of the top 20 most productive countries researching rice in Indonesia and the Philippines, see Supplementary Table 4 . Figure 7 shows a macro-level picture of how countries have collaborated on rice-related projects in Indonesia and the Philippines since 2001, suggesting that rice research in both countries has benefited from international partnerships.

A macro-level picture of how countries have collaborated on rice-related projects in Indonesia and the Philippines since 2001. It suggests that rice research in both countries has benefited from international partnerships.

Only around 47% (1050 studies) of our dataset acknowledged their funding sources, where most received financial support either from governments (45%), research (27%), or university funders (16%) (Fig. 8 ). To see the top 15 funders that supported at least 10 rice-related research projects in Indonesia and the Philippines from 2001 to 2021, refer to Supplementary Table 5 . Of over 150 rice research funders, Indonesia’s Ministry of Education, Culture, and Research (formerly the Ministry of Research and Technology) funded ~6% (62 out of 1050 studies). The Japan Society for the Promotion of Science and Japan’s Ministry of Education, Culture, Sports, Science and Technology came in as the second and third largest funders, respectively.

The majority of rice research projects in Indonesia and the Philippines were funded by governments (45%), research (27%), and university institutions (16%).

Half of all articles in the dataset were borne out of interdisciplinary collaboration. More than a quarter of the articles, however, were unidentified, showing an apparent undercount of the total number of disciplinary collaborations. Most of these collaborative pieces of work (~61%) belong to the natural science subject areas of agricultural and biological sciences; biochemistry, genetics, and molecular biology; and environmental science (see Table 2 ). Note that the cumulative number of articles in Table 2 is more than the total number of the sample dataset since an article may belong to multiple subject areas as indicated by its authors in Scopus. Less than 9% (354) of all papers were written by social scientists, highlighting their marginal contribution to rice research. The social studies of rice can increase our understanding of the many facets of rice production, including their socio-political, economic, and cultural aspects.

Our review shows that there are 10 major networks of rice research co-citations (Fig. 9 ). The papers by Bouman et al. ( 2005 ), Bouman et al. ( 2007 ), Bouman and Tuong ( 2001 ), and Tuong and Bouman ( 2003 ) were co-cited by scholars studying the relationship between water scarcity management vis-à-vis rice growth and yield (the purple cluster in Fig. 9 ). Papers by Yoshida et al. ( 2009 ), De Datta ( 1981 ), and Peng et al. ( 1999 ) were co-cited by scholars researching the genetic diversity, yield, and principles and practices of rice production in Indonesia (the red cluster in Fig. 9 ). Papers by Ou ( 1985 ), Mackill and Bonman ( 1992 ), Sambrook et al. ( 1989 ), Kauffman et al. ( 1973 ), Iyer and McCouch ( 2004 ), and Mew ( 1987 ) were considered essential references in studying rice diseases (blue cluster in Fig. 9 ). The top-cited article on rice research in Indonesia and the Philippines, based on their overall global citations, is a study on water-efficient and water-saving irrigation (Belder et al., 2004 ). This study detailed alternative options for typical water management in lowland rice cultivation, where fields are continuously submerged, hence requiring a continuous large amount of water supply (Belder et al., 2004 ). Global citations refer to the citations received by the articles within and beyond our sample dataset of 2243 articles. To see the top 10 most globally cited articles on rice research in Indonesia and the Philippines, refer to Supplementary Table 6 .

There are 10 major networks of rice research co-citations in Indonesia and the Philippines.

The journal Biodiversitas: Journal of Biological Diversity published the most number of papers on rice research in the two countries. Biodiversitas publishes papers “dealing with all biodiversity aspects of plants, animals, and microbes at the level of gene, species, ecosystem, and ethnobiology” (Biodiversitas, 2021 ). Following its indexing in Scopus in 2014, Biodiversitas has increasingly published rice studies, most of which were authored by Indonesian researchers. To see the top 10 most relevant journals for rice research in Indonesia and the Philippines based on the number of documents published since 2001, refer to Supplementary Table 7 .

Based on their local citations, the journals Field Crops Research , Theoretical & Applied Genetics , and Science are the most relevant. Field Crops Research focuses on crop ecology, crop physiology, and agronomy of field crops for food, fiber, feed, medicine, and biofuel. Theoretical and Applied Genetics publishes original research and review articles in all critical areas of modern plant genetics, plant genomics, and plant biotechnology. Science is the peer-reviewed academic journal of the American Association for the Advancement of Science and one of the world’s top academic journals. To see the top 30 most relevant journals for rice research in Indonesia and the Philippines based on the number of local citations, refer to Supplementary Table 8 .

The most used keywords found in 2243 rice research papers published between 2001 and 2021 in Indonesia and the Philippines are food security, climate change, drought, agriculture, irrigation, genetic diversity, sustainability, technical efficiency, and production (Fig. 10 ). We found 11 clusters across four significant themes of rice research in these countries (Fig. 11 ).

The most used keywords found in 2243 rice research papers published between 2001 and 2021 in Indonesia and the Philippines are food security, climate change, drought, agriculture, irrigation, genetic diversity, sustainability, technical efficiency, and production.

There are four major themes composed of 11 clusters of rice research in Indonesia and the Philippines since 2001.

Basic themes

We identified four major clusters under ‘basic themes’ (refer to Fig. 11 ):

The Red Cluster on studies in the Philippines related to rice yield and productivity, drought, nitrogen, the Green Revolution, and the use and potential of biomass;

The Blue Cluster on studies in Indonesia related to food security, climate change, agriculture, upland rice, irrigation, technical efficiency, and sustainability vis-à-vis rice production;

The Green Cluster on rice genetic diversity, bacterial blight diseases, resistant rice genes, aerobic rice, and brown planthoppers; and

The Gray Cluster on the nutritional aspects of rice, including studies on biofortified rice cultivars.

Agriculture suffers from climate change impacts and weather extremes. Rice researchers in Indonesia and the Philippines are identifying drought-tolerant rice cultivars that can produce high yields in abiotic stress-prone environments (Afa et al., 2018 ; Niones et al., 2021 ). These hybrid cultivars are vital for increasing rice productivity, meeting production demand, and feeding the growing Filipino and Indonesian populations (Kumar et al., 2021 ; Lapuz et al., 2019 ). Researchers have also looked at alternative nutrient and water management strategies that farmers can use, especially those in rainfed lowland areas during drought (Banayo, Bueno et al., 2018 ; Banayo, Haefele et al., 2018 ). There were also studies on the socio-cultural dynamics under which farmers adapt to droughts, such as how past experiences of hazards influence farmers’ perceptions of and actions toward drought (Manalo et al., 2020 ).

Motor themes

We identified three significant clusters of ‘motor themes’ (refer to Fig. 11 ):

The Pink Cluster on yield loss and integrated pest management of rice fields;

The Blue-Green Cluster on biodiversity, ecosystem services, remote sensing, and water productivity; and

The Orange Cluster on the antioxidant properties of rice bran and black rice.

In both countries, pests, including weeds (Awan et al., 2014 , 2015 ), insects (Horgan et al., 2018 , 2021 ), and rodents (Singleton, 2011 ; Singleton et al., 2005 , 2010 ), have significant impacts on yield loss in rice production and human health. To address these, many farmers have embraced chemical-heavy pest management practices to prevent yield loss and increase economic benefits. Pesticides began their use in Indonesia and the Philippines and rapidly expanded from the 1970s to the 1980s (Resosudarmo, 2012 ; Templeton and Jamora, 2010 ). However, indiscriminate use of pesticides caused an ecological imbalance that exacerbated pest problems (Templeton and Jamora, 2010 ) and contributed to farmers’ acute and chronic health risks (Antle and Pingali, 1994 ; Pingali and Roger, 1995 ).

Integrated pest management was introduced, applied, and studied in both countries to address these issues. This approach combines multiple compatible pest control strategies to protect crops, reduce pesticide use, and decrease farming costs (Gott and Coyle, 2019 ). For example, Indonesia’s 1989 National Integrated Pest Management Program trained hundreds of thousands of farmers and agricultural officials about its principles, techniques, and strategies (Resosudarmo, 2012 ). In the Philippines, the government of then-President Fidel V. Ramos (1992–1996) prohibited using hazardous pesticides and instituted a “multi-pronged approach to the judicious use of pesticides” (Templeton and Jamora, 2010 , p. 1). President Ramos’ suite of policies included deploying Integrated Pest Management “as a national program to encourage a more ecologically sound approach to pest control” (Templeton and Jamora, 2010 , p. 1). This pesticide policy package benefited the Philippine government in terms of private health costs avoided (Templeton and Jamora, 2010 ).

To address weed problems, farmers traditionally use manual weeding, a labor-intensive practice. However, as labor costs for manual weeding increased, herbicide use became economically attractive to farmers (Beltran et al., 2012 ). Herbicide experiments were made to address common rice weeds including barnyard grass ( Echinochloa crus-galli ) (Juliano et al., 2010 ), crowfoot grass ( Dactyloctenium aegyptium ) (Chauhan, 2011 ), three-lobe morning glory ( Ipomoea triloba ) (Chauhan and Abugho, 2012 ), and jungle rice ( Echinochloa colona ) (Chauhan and Johnson, 2009 ). Knowledge gained from these experiments contributed to the development of integrated weed management strategies.

Yet, many factors come into play when farmers decide to use herbicides. Beltran et al. ( 2013 ) reported that farmers’ age, household size, and irrigation use are significant determinants of adopting herbicides as an alternative to manual weeding. Beltran et al. ( 2013 ) further showed that economic variables, like the price of the herbicide, household income, and access to credit, determined farmers’ level of herbicide use (Beltran et al., 2013 ). Their study highlights the complex decision-making process and competing factors affecting weed management in the Philippines.

Apart from weeds, insects, like brown planthoppers ( Nilaparvata lugens ) and green leafhoppers ( Cicadella viridis ) and their accompanying diseases, affect rice production. In Java, Indonesia, Triwidodo ( 2020 ) reported a significant influence between the insecticide use scheme and the brown planthopper ( Nilaparvata lugens ) attack rates in rice fields. Brown planthopper attacks increased depending on the frequency of pesticide application, their varieties, and volume (Triwidodo, 2020 ). In the Philippines, Kim and colleagues ( 2019 ) developed a rice tungro epidemiological model for a seasonal disaster risk management approach to insect infestation.

Some social studies of integrated pest management included those that looked at the cultural practices that mitigate insect pest losses (Litsinger et al., 2011 ) and farmers’ knowledge, attitudes, and methods to manage rodent populations (Stuart et al., 2011 ). Other social scientists evaluated the value of amphibians as pest controls, bio-monitors for pest-related health outcomes, and local food and income sources (Propper et al., 2020 ).

Niche themes

We identified two ‘niche themes’ consisting of studies related to (a) temperature change and (b) organic rice production (refer to Fig. 11 ). Temperature change significantly affects rice farming. In the Philippines, Stuecker et al. ( 2018 ) found that El Niño-induced soil moisture variations negatively affected rice production from 1987–2016. According to one experiment, high night temperature stress also affect rice yield and metabolic profiles (Schaarschmidt et al., 2020 ). In Indonesia, a study suggests that introducing additional elements, such as Azolla, fish, and ducks, into the rice farming system may enhance rice farmers’ capacity to adapt to climate change (Khumairoh et al., 2018 ). Another study produced a rainfall model for Malang Regency using Spatial Vector Autoregression. This model is essential as rainfall pattern largely determines the cropping pattern of rice and other crops in Indonesia (Sumarminingsih, 2021 ).

Studies on organic rice farming in the Philippines include resource-poor farmers’ transition from technological to ecological rice farming (Carpenter, 2003 ) and the benefits of organic agriculture in rice agroecosystems (Mendoza, 2004 ). Other studies on organic rice focused on its impacts on agricultural development (Broad and Cavanagh, 2012 ) and climate resilience (Heckelman et al., 2018 ). In Indonesia, Martawijaya and Montgomery ( 2004 ) found that the local demand for organic rice produced in East Java was insufficient to generate revenue enough to cover its production costs. In West Java, Komatsuzaki and Syuaib ( 2010 ) found that organic rice farming fields have higher soil carbon storage capacity than fields where rice is grown conventionally. In Bali, farmers found it challenging to adopt organic rice farming vis-à-vis the complex and often contradictory and contested administration of the Subaks (MacRae and Arthawiguna, 2011 ) and the challenges they have to confront in marketing their produce (Macrae, 2011 ).

Emerging or declining themes

We identified two clusters of ‘emerging/declining themes’ or areas of rice research that are weakly developed and marginal (refer to Fig. 11 ). The Purple Cluster (emerging) studies rice straw, rice husk, methane, and rice cultivation, while the Light Blue Cluster (declining) pertains to local rice research.

In this section, we present and discuss the contributions of the social sciences, highlight key gaps, and provide a research agenda across six interdisciplinary areas for future studies. In Table 3 , we summarized the various topic areas that other scholars could focus on in their future studies of rice in Indonesia and the Philippines.

Economic, political, and policy studies

Political scientist Ernest A. Engelbert ( 1953 ) was one of the earliest scholars to summarize the importance of studying agricultural economics, politics, and policies. Engelbert ( 1953 ) identified three primary reasons scholars and laypeople alike need to understand the nature of political processes in agriculture. First, the rapid change and highly contested political environment where agriculture operates often places agriculture last on national policy agenda. Second, the formulation of agricultural policies intersects with contemporary national and economic contexts by which these policies revolve. Third, understanding the political processes around agriculture can help avoid political pressures and machinations aimed at undermining agricultural development.

Politics play a crucial role in better understanding rice- and agriculture-related policies, their evolution, dynamics, challenges, developments, and futures. Grant ( 2012 , p. 271) aptly asks, “Who benefits [from government policies, regulations, and programs]?” . Knowing, understanding, and answering this question is crucial since policymaking is a highly contested process influenced and negotiated not only by farmers and decision-makers but also by other interest groups, such as people’s organizations and non-government organizations. On the other hand, understanding macro- and micro-economic government arrangements come hand-in-hand in analyzing how policies impact farmers and consumers. Using tariffs as an example, Laiprakobsup ( 2014 , p. 381) noted the effects of government interventions in the agrarian market:

“… when the government implements consumer subsidy programs by requiring the farmers to sell their commodities at a cheaper price, it transfers the farmers’ incomes that they were supposed to earn to the consumers. Moreover, the government transfers tax burdens to the farmers via export taxes in that the agricultural industry is likely to purchase the farmers’ commodities as cheaply as possible in order to make up for its cost.”

The two countries have compelling economic, political, and policy-oriented rice studies. Some examples of this type of research in the Philippines are the following. Intal and Garcia ( 2005 ) argued that the price of rice had been a significant determinant in election results since the 1950s. Fang ( 2016 ) analyzed how the Philippines’ colonial history bolstered an oligarchy system, where landed elite politicians and patronage politics perpetuated corruption to the detriment of rice farmers. Balié and Valera ( 2020 ) examined rice trade policy reforms’ domestic and international impacts. San Juan ( 2021 ) contends that the 2019 Rice Tariffication Law of the Philippines only encouraged the country to rely on imports and failed to make the local rice industry more competitive.

In Indonesia, some political studies on rice production are the following. Putra et al. ( 2020 ) analyzed how urbanization affected food consumption, food composition, and farming performance. Noviar et al. ( 2020 ) provided evidence that households in the rice sub-sector have achieved an insufficient level of commercialization in their rice production. Rustiadi et al. ( 2021 ) investigated the impacts of land incursions over traditionally rice farming regions due to Jakarta’s continuous expansion. Satriawan and Shrestha ( 2018 ) evaluated how Indonesian households participated in the Raskin program, a nationwide rice price subsidy scheme for the poor. Misdawita et al. ( 2019 ) formulated a social accounting matrix and used a microsimulation approach to assess the impacts of food prices on the Indonesian economy.

Future work

Social science researchers could further explore and compare the local, regional, and national similarities and differences of the abovementioned issues or conduct novel research related to land-use change, land management, urbanization, food and agricultural policies, trade policies, irrigation governance, and price dynamics. Comparative social studies of rice could also lead to meaningful results. As social policy scholar Linda Hantrais noted:

“Comparisons can lead to fresh, exciting insights and a deeper understanding of issues that are of central concern in different countries. They can lead to the identification of gaps in knowledge and may point to possible directions that could be followed and about which the researcher may not previously have been aware. They may also help to sharpen the focus of analysis of the subject under study by suggesting new perspectives.” (Hantrais, 1995 , p. n/a).

Sociological, anthropological, and cultural studies

Biologists dominated agricultural research until the mid-1960s (Doorman, 1991 ). Agriculture, in other words, was no social scientist’s business. However, this situation gradually changed when governments and scholars realized the long-term impacts of the Green Revolution from the 1950s to the 1980s, which underscores that the development, transfer, and adoption of new agrotechnology, especially in developing countries, is driven not only by techno-biological factors but also by the socio-economic, political, and cultural realities under which the farmers operate. Since then, sociologists, anthropologists, and cultural scholars have become indispensable in answering the “how”, “what”, and “why” agrarian communities follow, adopt, utilize, or, in some cases, prefer local/traditional production technologies over the technological and scientific innovations developed by engineers, biologists, geneticists, and agriculturists. Nyle C. Brady, a soil scientist and the former Director-General of the IRRI pointed out:

“… we increasingly recognize that factors relating directly to the farmer, his family, and his community must be considered if the full effects of agricultural research are to be realized. This recognition has come partly from the participation of anthropologists and other social scientists in interdisciplinary teams … during the past few years.” (IRRI, 1982 ).

Since the late 19th century, many rice studies have tried to answer the roles of social scientists in agricultural research. Social sciences have contributed to agricultural research in many ways, especially regarding technology adoption by farmers (DeWalt, 1985 ; Doorman, 1990 ). Doorman ( 1991 , p. 4) synthesized these studies and offered seven roles for sociologists and anthropologists in agricultural research as follows:

“Accommodator of new technology, ex-post and ex-ante evaluator of the impact of new technology, an indicator of the needs for new technology, translator of farmer’s perceptions, broker-sensitizer, adviser in on-farm research, and trainer of team members from other disciplines.”

Social studies of rice are especially critical in Indonesia and the Philippines—home to hundreds of Indigenous cultural communities and Indigenous peoples (Asian Development Bank, 2002 ; UNDP Philippines, 2010 ). Regardless of the highly contested debates surrounding “indigeneity” or “being indigenous,” especially in Indonesia (Hadiprayitno, 2017 ), we argue that Indigenous cultural communities and Indigenous peoples have similarities (i.e., they are often farming or agrarian societies) but also recognize their differences and diversity in terms of their farming practices, beliefs, traditions, and rituals. These socio-cultural factors and human and non-human interactions influence rice production; thus, these differences and diversity bring front-and-center the importance of needs-based, community-driven, and context-sensitive interventions or projects for rice farming communities. These are research areas best explored by sociologists, anthropologists, and cultural scholars.

Today, agriculture’s sociological, anthropological, and cultural research have gone beyond the classic technology adoption arena. In Indonesia, studies have explored farmers’ technical efficiency in rice production (e.g., Muhardi and Effendy, 2021 ), the similarities and differences of labor regimes among them (e.g., White and Wijaya, 2021 ), the role of social capital (e.g., Salman et al., 2021 ), and the reciprocal human–environmental interactions in the rice ecological system (e.g., Sanjatmiko, 2021 ). Disyacitta Nariswari and Lauder ( 2021 ) conducted a dialectological study to examine the various Sundanese, Javanese, and Betawi Malay words used in rice production. Rochman et al. ( 2021 ) looked into the ngahuma (planting rice in the fields) as one of the inviolable customary laws of the Baduy Indigenous cultural community in Banten, Indonesia.

In the Philippines, Balogbog and Gomez ( 2020 ) identified upland rice farmers’ productivity and technical efficiency in Sarangani. Aguilar et al. ( 2021 ) examined the drivers of change, resilience, and potential trajectories of traditional rice-based agroecosystems in Kiangan, Ifugao. Pasiona et al. ( 2021 ) found that using the “modified listening group method” enables farmers’ peer-to-peer learning of technical concepts. Sociologist Shunnan Chiang ( 2020 ) examined the driving forces behind the transformation of the status of brown rice in the country.

Social scientists could further look into the social, cultural, technological, and human–ecological interactions in the temporal and spatial studies of different rice farming regions in Indonesia and the Philippines. Other topics could include the cultural practices and the techno-social relationships of rice farmers (e.g., Shepherd and McWilliam, 2011 ) and other players in the rice value chain, local and indigenous knowledge and practices on agrobiodiversity conservation, historical and invasive pests and diseases, agricultural health and safety, farmer education, and aging agricultural infrastructures. Lastly, future researchers can explore the impacts of adopting rice farming technologies in the different stages or processes of the rice value chain. They can look into its short- and longer-term effects on farmers’ livelihoods and conduct comparative analyses on how it improves, or not, their livelihoods, and whether farmers regard them better compared to the traditional and indigenous practices and beliefs that their communities apply and observe in rice farming.

Social and environmental psychology

Our review yielded no article published on the social and environmental psychology aspects of rice farming in Indonesia and the Philippines, suggesting a new research frontier. The increasing demand for and competition over agricultural and natural resources due to climate change and population expansion (Foley et al., 2011 ) opens up new and emerging sociopsychological dilemmas for society to understand, answer, and, hopefully, solve. Social and environmental psychologists can help shed light on these questions, such as those related to understanding farmers’ pro-environmental agricultural practices (Price and Leviston, 2014 ), sustainable sharing and management of agricultural and natural resources (Anderies et al., 2013 ; Biel and Gärling, 1995 ), and understanding the psychosocial consequences of resource scarcity (Griskevicius et al., 2013 ). Broadly, social psychology examines human feelings, thoughts, and behaviors and how they are influenced by the actual, imagined, and implied presence, such as the effects of internalized social norms (Allport, 1985 ). Social psychologists look at the many facets of personality and social interactions and explore the impacts of interpersonal and group relationships on human behavior (American Psychological Association, 2014b ). On the other hand, environmental psychology examines psychological processes in human encounters with their natural and built environments (Stern, 2000 ). Environmental psychologists are interested in studying and understanding people’s responses to natural and technological hazards, conservation, and perceptions of the environment (American Psychological Association, 2014a ).

Using the Asian Journal of Social Psychology and the Journal of Environmental Psychology as benchmarks, we recommend that scholars explore the following uncharted or least studied areas of rice research in Indonesia and the Philippines: sociopsychological processes such as attitude and behavior, social cognition, self and identity, individual differences, emotions, human–environmental health and well-being, social influence, communication, interpersonal behavior, intergroup relations, group processes, and cultural processes. Researchers could also investigate the psycho-behavioral areas of nature–people interactions, theories of place, place attachment, and place identity, especially in rice farming. Other topics may include farmers’ perceptions, behaviors, and management of environmental risks and hazards; theories of pro-environmental behaviors; psychology of sustainable agriculture; and the psychological aspects of resource/land management and land-use change.

Climate change, weather extremes, and disaster risk reduction

Indonesia’s and Philippines’ equatorial and archipelagic location in the Pacific Ring of Fire (Bankoff, 2016 ; Parwanto and Oyama, 2014 ), coupled with their political, social, and economic complexities (Bankoff, 2003 , 2007 ; UNDRR and CRED, 2020 ), expose and render these countries highly vulnerable to hazards, such as typhoons, strong winds, tsunamis, storm surges, floods, droughts, and earthquakes. The accelerating global climate change increases the frequency and intensity of some of these hazards, such as prolonged droughts, torrential rainfalls causing floods, and super typhoons (IPCC, 2014 ). For example, torrential flooding, induced by heavy rains caused by low pressures and southwest monsoons, has been damaging lives and livelihoods, including rice production (Statista, 2021 ). The 2020 droughts caused over 12 trillion pesos (~US$239.40 billion) of economic losses in the Philippines (Statista, 2021 ) and affected millions of Indonesians (UNDRR, 2020 ). Prolonged drought in Indonesia has also exacerbated fire hazards, which caused transboundary haze pollution in neighboring countries, like Singapore and the Philippines, inflecting environmental health damages (Aiken, 2004 ; Sheldon and Sankaran, 2017 ; Tan-Soo and Pattanayak, 2019 ). Increasing sea-level rise due to anthropogenic climate change puts cities like Jakarta and Manila at risk of sinking in the next 30–50 years (Kulp and Strauss, 2019 ). The high vulnerability, frequent exposure, and low capacities of marginalized and poor Indonesians and Filipinos turn these hazards into disasters (Gaillard, 2010 ; Kelman, 2020 ; Kelman et al., 2015 ), negatively affecting rice agriculture.

Given these contexts, climate change, weather extremes, and disaster risks, vis-à-vis its impacts on the rice sector, are issues of profound interest to scholars and the Indonesian and Philippine governments. In the Philippines, climate adaptation studies include re-engineering rice drying systems for climate change (Orge et al., 2020 ) and evaluating climate-smart farming practices and the effectiveness of Climate-Resiliency Field Schools in Mindanao (Chandra et al., 2017 ). In Indonesia, where some rice farming communities are vulnerable to sea-level rise, scholars are experimenting to identify rice cultivars with high yields under different salinity levels (Sembiring et al., 2020 ). Hohl et al. ( 2021 ) used a regional climate model to develop index-based drought insurance products to help the Central Java government make drought-related insurance payments to rice farmers. Aprizal et al. ( 2021 ) utilized land-use conditions and rain variability data to develop a flood inundation area model for the Way Sekampung sub-watershed in Lampung, Sumatra. Others also looked at the science behind liquefaction hazards caused by irrigation systems for wet rice cultivation in mountainous farming communities like the 2018 earthquake-triggered landslides in Palu Valley, Sulawesi (Bradley et al., 2019 ).

Examples of climate mitigation-related studies in the Philippines include investigating the social innovation strategies in engaging rice farmers in bioenergy development (Minas et al., 2020 ) and evaluating the environmental performance and energy efficiency of rice straw-generated electricity sources (Reaño et al., 2021 ). Doliente and Samsatli ( 2021 ) argue that it is possible to combine energy and food production to increase farm productivity and reduce GHG emissions with minimal land expansion. Other studies have looked into the potential of alternate wetting and drying irrigation practices to mitigate emissions from rice fields (Sander et al., 2020 ).

Future work could explore the following topic areas: demand-driven research and capacity building on climate information and environmental monitoring; nature-based solutions for climate mitigation and adaptation; water–energy–food nexus in rice farming; the nexus of climate change and conflict in rice farming communities; the potentials and pitfalls of social capital in farmer’s everyday adaptation; just energy transitions in rice farming; vulnerabilities from and traditional/local/indigenous ways of adapting to climate change, including the various learning strategies communities use for its preservation; and examples, potentials, and barriers in adopting climate-smart agriculture technologies and practices.

Demographic transitions and aging farmers

Farmers are in various stages and speeds of aging globally (Rigg et al., 2020 ). Evidence of aging farmers in the Global North has been reported in Australia (O’Callaghan and Warburton, 2017 ; Rogers et al., 2013 ), the Czech Republic (Zagata et al., 2015 ), England (Hamilton et al., 2015 ), Japan (Poungchompu et al., 2012 ; Usman et al., 2021 ), and the United States of America (Mitchell et al., 2008 ; Reed, 2008 ; Yudelman and Kealy, 2000 ). Similarly, in the Global South, HelpAge International ( 2014 , p. 21) reported that “there has been a universal trend of an increase in the proportion of older people… attached to agricultural holdings… across [Low and Middle-income Countries in] Asia, sub-Saharan Africa, Latin America, and the Caribbean.” Moreover, farming populations are aging rapidly in East and Southeast Asia (Rigg et al., 2020 ) and southern Africa (HelpAge, 2014 ). Despite this, the literature on aging farmers in Southeast Asian countries remains scant, except for case studies conducted in some villages and provinces in Thailand (Poungchompu et al., 2012 ; Rigg et al., 2018 , 2020 ) and the Philippines (Moya et al., 2015 ; Palis, 2020 ).

Rice farmers’ quiet but critical demographic transformation in Indonesia and the Philippines has not received much attention from scientists, policymakers, and development practitioners. The impacts of aging farmers on the micro-, meso-, and macro-level agricultural processes and outcomes are important issues that require urgent attention. Studies done in other countries could guide future work to explore these questions in Indonesia and the Philippines. These include aging’s potential negative implications in terms of agricultural efficiency and productivity (e.g., Tram and McPherson ( 2016 ) in Vietnam, and Szabo et al. ( 2021 ) in Thailand), food security (e.g., Bhandari and Mishra ( 2018 ) in Asia), farming continuity and sustainability (e.g., O’Callaghan and Warburton ( 2017 ) in Australia, Palis ( 2020 ) in the Philippines, and Rigg et al. ( 2018 , 2020 ) in Thailand), aging and feminization of farm labor (e.g., Liu et al. ( 2019 ) in China), cleaner production behaviors (e.g., Liu et al. ( 2021 ) in Northern China), youth barriers to farm entry (e.g., Zagata and Sutherland ( 2015 ) in Europe), and health and well-being of aging farmers (Jacka, 2018 ; Rogers et al., 2013 ; Ye et al., 2017 ).

Other critical new topics include the (dis)engagement and re-engagement of young people in rice farming; gender dynamics—including structures and systems of inclusion and/or exclusion—in rice production; the impacts of migration and return migration to farming households; community-based and policy-oriented case studies that provide examples of successfully engaging and retaining youth workers in farming; and social protection measures for aging farmers, to name a few.

Contemporary and emerging challenges

One of the biggest and most visible contemporary global challenges is the Covid-19 pandemic. Most pronounced is the pandemic’s impacts on the healthcare system and the economic toll it caused on the lives and livelihoods of people, including rice farmers. Only 0.18% (4 articles) of our dataset have investigated the impacts of Covid-19 on rice systems in Indonesia and the Philippines. Ling et al. ( 2021 ) assessed the effects of the pandemic on the domestic rice supply vis-à-vis food security among ASEAN member-states. They found that Singapore and Malaysia were highly vulnerable to a pandemic-induced rice crisis, while Brunei, Indonesia, and the Philippines are moderately vulnerable. They argued that Southeast Asian rice importers should consider alternative import strategies to reduce their high-risk reliance on rice supply from Thailand and Vietnam and look for other suppliers in other continents.

Rice prices did not change in the early months of the pandemic in Indonesia (Nasir et al., 2021 ); however, as the health emergency progressed, distributors and wholesalers incurred additional costs due to pandemic-induced mobility restrictions (Erlina and Elbaar, 2021 ). In the Philippines, San Juan ( 2021 ) argues that the global rice supply disruption due to the pandemic proves that the country cannot heavily rely on rice imports; instead, it should work on strengthening its domestic rice supply. To realize this, he recommended drastic investments in agriculture and research, rural solar electrification, and the promotion of research on increasing rice yields, boosting productivity, and planting sustainably as feasible steps on the road to rice self-sufficiency.

The ways and extent to which the pandemic negatively affected or exacerbated the vulnerabilities of rice farmers and other value chain actors remain an understudied area in the social studies of rice. Scholars could study the pandemic’s impacts in conjunction with other contemporary and emerging challenges like climate change, weather extremes, aging, conflict, and poverty. Scholars could also explore the medium- and longer-term impacts of the pandemic on rice production, unemployment risks, rice supply and nutrition security of farming households, and the potential and extent to which economic stimulus can benefit rice farmers, to name a few. Most importantly, the pandemic allows researchers and governments to assess the business-as-usual approach that resulted in the disastrous impacts of the pandemic on different sectors, including rice farmers, and hopefully devise strategies to learn from these experiences.

From our review of 2243 articles, cumulatively written by 6893 authors using almost 80,000 references, we conclude that a voluminous amount of rice research has been conducted in Indonesia and the Philippines since 2001. As in other reviews, (e.g., on energy research by Sovacool, 2014 ), our results show that women scholars remain underrepresented in rice research in Indonesia and the Philippines. While interdisciplinary collaboration is abundant, most of these studies belong to the natural sciences with minimal contributions from the social sciences, arts, and humanities. University and research institutions contributed the most to rice research in Indonesia and the Philippines: from hybrid rice cultivars, water management, and technology adoption to socio-cultural, political, economic, and policy issues. Influential scholars in the field were affiliated with the IRRI, which can be expected given the institute’s focus on rice, and key agriculture-focused universities and government bureaus such as the University of the Philippines and the PhilRice in the Philippines, and the Institut Pertanian Bogor University and the Universitas Gadja Maja in Indonesia. We also discussed some examples of economic, political, and policy studies; social, anthropological, and cultural research; social and environmental psychology; climate change, weather extremes, and disaster risk reduction; demographic transitions; and contemporary and emerging issues and studies on rice in the two Southeast Asian countries. Ultimately, we hope that this systematic review can help illuminate key topic areas of rice research in Indonesia and the Philippines and magnify the crucial contributions from and possible research areas and questions that interdisciplinary and comparative social scientists can further explore.

Data availability

The dataset analyzed in this study is available in the Figshare online repository via https://doi.org/10.6084/m9.figshare.17284814.v2 . All codes about Bibliometrix are available at https://bibliometrix.org/ .

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The work described in this paper was substantially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKUST 26600521). Partial funding was also made available by the HKUST Institute for Emerging Market Studies with support from EY.

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Cuaton, G.P., Delina, L.L. Two decades of rice research in Indonesia and the Philippines: A systematic review and research agenda for the social sciences. Humanit Soc Sci Commun 9 , 372 (2022). https://doi.org/10.1057/s41599-022-01394-z

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Climate variability impacts on rice production in the Philippines

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Visualization, Writing – review & editing

Affiliations Center for Climate Physics, Institute for Basic Science (IBS), Busan, Republic of Korea, Pusan National University, Busan, Republic of Korea

Roles Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Atmospheric Sciences, University of Washington, Seattle, WA, United States of America

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Tropical Plant & Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, United States of America

• Malte F. Stuecker, 
• Michelle Tigchelaar, 
• Michael B. Kantar

• Published: August 9, 2018
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Changes in crop yield and production over time are driven by a combination of genetics, agronomics, and climate. Disentangling the role of these various influences helps us understand the capacity of agriculture to adapt to change. Here we explore the impact of climate variability on rice yield and production in the Philippines from 1987–2016 in both irrigated and rainfed production systems at various scales. Over this period, rice production is affected by variations in soil moisture, which are largely driven by the El Niño–Southern Oscillation (ENSO). We found that the climate impacts on rice production are strongly seasonally modulated and differ considerably by region. As expected, rainfed upland rice production systems are more sensitive to soil moisture variability than irrigated paddy rice. About 10% of the variance in rice production anomalies on the national level co-varies with soil moisture changes, which in turn are strongly negatively correlated with an index capturing ENSO variability. Our results show that while temperature variability is of limited importance in the Philippines today, future climate projections suggest that by the end of the century, temperatures might regularly exceed known limits to rice production if warming continues unabated. Therefore, skillful seasonal prediction will likely become increasingly crucial to provide the necessary information to guide agriculture management to mitigate the compounding impacts of soil moisture variability and temperature stress. Detailed case studies like this complement global yield studies and provide important local perspectives that can help in food policy decisions.

Citation: Stuecker MF, Tigchelaar M, Kantar MB (2018) Climate variability impacts on rice production in the Philippines. PLoS ONE 13(8): e0201426. https://doi.org/10.1371/journal.pone.0201426

Editor: Vanesa Magar, Centro de Investigacion Cientifica y de Educacion Superior de Ensenada Division de Fisica Aplicada, MEXICO

Received: February 18, 2018; Accepted: July 16, 2018; Published: August 9, 2018

Copyright: © 2018 Stuecker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data was sourced from the following third party providers: Rice production data from 1987-2016 were obtained from the Philippine government statistic authority ( http://countrystat.psa.gov.ph/ ). ENSO variability was characterized using the Niño3.4 (N3.4) index, which is calculated as the area averaged sea surface temperature anomalies from HadISST1 ( https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html ) in the region 170°W-120°W and 5°S-5°N. Soil moisture data were obtained from CPC (version 2) at 0.5º horizontal resolution (35) ( https://www.esrl.noaa.gov/psd/data/gridded/data.cpcsoil.html ). Surface air temperature (2m) was obtained from the ERA-Interim reanalysis on a 0.125º horizontal grid ( https://www.ecmwf.int/ ). Future climate projection data were obtained from the CMIP5 database for the business-as-usual scenario RCP 8.5 ( https://cmip.llnl.gov/cmip5/data_portal.html ).

Funding: MFS was supported by the Institute for Basic Science (project code IBS- R028-D1) and the NOAA Climate and Global Change Postdoctoral Fellowship Program, administered by UCAR's Cooperative Programs for the Advancement of Earth System Sciences (CPAESS) and MT was funded by a grant from the Tamaki Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Rice–which provides nearly half the calories for half the world’s population [ 1 ; 2 ]–is a key crop for the Philippines: it is a staple food (with >110 kg/person/year consumption, [ 3 ], http://irri.org/rice-today/nourishing-a-nation ), the sixth highest per capita consumption in the world), as well as a major source of income (rice production valued at ~6 billion U.S. dollars in 2015; [ 4 ]). The Philippines produces approximately 3% of the world’s rice in both “lowland” flooded transplanted paddies and “upland” rainfed direct seeded areas [ 5 ]. As such, understanding what drives changes in rice production in the Philippines is essential for meeting current and future food security [ 6 ; 7 ]. Variations in crop yields can be explained by either endogenous drivers, such as genetics (including breeding methods–pure line, synthetic, hybrid) and agronomy (including technology–use of fertilizer, irrigation, machinery) [ 6 ; 8 ; 9 ], and exogenous forcing such as climate variability, which has been reported to decrease the influence of genetics [ 10 ]. The role of climate is becoming increasingly important due to anthropogenic climate change, which could drastically change local environments, damage yields [ 11 ; 12 ], and influence the yield stability of staple crops [ 13 ; 14 ]. Here we assess how current and future climate variability influences the various modes of rice production in the Philippines.

Continuing to feed a growing world population expected to reach ~9 billion by 2050 [ 15 ] while faced with a changing climate is a tremendous challenge. To date, global food production has steadily increased through innovations in agricultural technology (improved practices and genetics). The Philippines has mirrored global trends, with population increasing from ~26 million in 1960 to ~101 million in 2015, and rice production increasing from ~3.9 million tonnes in 1961 to ~19.0 million tonnes in 2014. This large improvement has been due to increased yields (production per unit area) and increased acreage being placed into production [ 16 ]. However, it is unclear whether it will be possible to sustain increasing production into the future [ 17 ], and if the changing land use patterns for agriculture are sustainable [ 18 ].

The Philippines is a large and spatially heterogeneous country, consisting of 7107 islands divided into 18 political regions and 81 provinces. There are four major climate regimes: 1) distinct wet monsoon and dry season, 2) no distinct dry season but a strong wet monsoon season, 3) intermediate between type 1 and 2, where there is a short wet monsoon and short dry season, and 4) an even distribution of rainfall throughout the year [ 19 ]. Planting dates vary between regions based largely on differences in climate ( S1 Table ). While rice in the Philippines is grown throughout the year ( S1 Table ), the largest production share is grown during the wet season. Due to this diversity of planting and harvesting, the government of the Philippines takes annual, semester, and quarterly statistics on rice production and harvested areas. Farms in the Philippines are generally small (less than two hectares on average; [ 20 ]), which may limit the implementation of advanced farming technologies. Currently, irrigated paddy rice accounts for 60% of total production [ 21 ], with the remainder grown as upland directly seeded rice.

In the Philippines, the dominant climate influence on inter-annual timescales is from the El Niño–Southern Oscillation (ENSO). ENSO has pronounced effects on global rainfall and temperature variability, particularly in the Indo-Pacific region [ 22 ; 23 ; 24 ]. It has been shown that this inter-annual climate variability can drastically impact crop yields and production globally [ 14 ; 25 ; 26 ]. In the tropical western Pacific region, El Niño events (the warm phase of ENSO) generally have a negative effect on farming. Specifically, El Niño induced droughts in the western Pacific have detrimentally affected Indonesian rice production [ 27 ], with worsening effects projected in response to greenhouse gas forcing [ 28 ]. Previous work on ENSO in the Philippines has shown that dry-season rice production is negatively impacted by El Niño on Luzon Island [ 25 ]. Additionally, tropical cyclones are a source of weather variability that is strongly seasonally modulated and exhibits localized impacts, suggesting that climate-yield and climate-production relationships need to be evaluated regionally and on sub-annual timescales.

An important limiting factor to increased food production in response to population growth and dietary shifts in the next century is the ability of crops to respond to climate variability, for instance soil moisture, surface temperatures, and the frequency of severe storms [ 29 ; 30 ]. Studies of climate impacts on crops typically either use process-based crop models, or evaluate the statistical relationship between crop production and climate variability in the past. Here we use this latter method to evaluate the impact of climate variability on rice production in the Philippines in different spatial and temporal contexts, and compare the range of past climate variability to projected future climate change to assess whether these relationships can be expected to hold in the future. We find that using a finer temporal and spatial resolution provides a more detailed understanding of climatic drivers of rice production, especially for upland (rainfed) rice, which is significantly impacted by ENSO through modifications in soil moisture. By the end of the century, temperatures will likely exceed present-day ranges, and will thus become an additional limiting factor to rice yield and production.

Materials and methods

Data acquisition.

Rice production data from 1987–2016 were obtained from the Philippine government statistic authority ( http://countrystat.psa.gov.ph/ ) for each political region and nationwide. Area harvested (hectares) and production (metric tonnes) data were collected from each political region and for the whole country for each quarter and year, for both irrigated and rainfed rice production. Missing data (where survey data was not complete) were linearly interpolated for each region (harvested area and production for rainfed systems) on the quarterly data (less than 1% of the data were missing). No values were missing for irrigated systems. Yield (tonnes per hectare) was calculated by dividing production by area for each quarter from 1987–2016.

To explore the ecological tolerance of rice we obtained the locality information of accessions stored in gene banks worldwide from https://www.genesys-pgr.org for tropical localities (from 23.5°S-23.5°N). From geo-referenced coordinates, we obtained surface temperature data for tropical rice from the WorldClim database at 30 arc seconds resolution [ 31 ], which were used to explore the climatic space inhabited by tropical rice.

Yield normalization

We created continuous time series of production and yield (rainfed and irrigated) for each aggregated political region. To remove the effect of yield increases due to breeding methods, we removed a ~7 year (27 quarters) running mean from each continuous time series and afterwards removed the residual total mean to construct an anomalous time series with zero mean. The results were qualitatively stable to the choice of the running mean window size (a 5 year window was also tested, data not shown). These normalization timescales are commonly used in the literature [ 11 ; 32 ] and correspond to a normal life cycle of a rice genotype used in farming [ 33 ].

Climate data

To calculate climate anomalies, we removed both the annual cycle (1987–2016 climatology) and the linear trend from each of the climate variables used. ENSO variability was characterized using the Niño3.4 (N3.4) index, which is calculated as the area averaged sea surface temperature anomalies from HadISST1 [ 34 ] in the region 170°W-120°W and 5°S-5°N. Soil moisture data were obtained from CPC (version 2) at 0.5° horizontal resolution [ 35 ]. Surface air temperature (2m) was obtained from the ERA-Interim reanalysis [ 36 ] on a 0.125° horizontal grid. For the global warming projections (see below), the present-day reference temperatures were obtained from the CRU TS version 3.23 dataset, which presents monthly data from the period 1901–2014 on a 0.5° horizontal grid [ 37 ]. To evaluate crop-climate relationships at the different spatial scales, climate data were either spatially averaged for the entire Philippines (here defined by the geographical region 117°E-128°E, 4°N-22°N) or the respective regions (see S1 Table ).

Climate projections

Future climate projection data were obtained from the CMIP5 database [ 38 ] for the business-as-usual scenario RCP 8.5. Monthly output was obtained from eighteen climate models and interpolated using bilinear interpolation to a 0.5° resolution common grid. For the 2°C and 4°C warming targets, we first constructed the canonical global warming temperature pattern [ 39 ] for each of the eighteen models by taking the difference in monthly climatology between the 2080–2099 and 1980–1999 time periods, normalized by the global, annual mean temperature change. The future climate projections are then calculated by adding the change in each (2°C or 4°C warmer) model climatology to the observed (1911–2010) climate history, thus preserving the present-day interannual temperature variability [ 40 ].

Correlation analysis

We utilize standard correlation analysis to investigate the relationships between the respective climate variables and rice production and yield. For these relationships, we consider seasonal anomalies to be independent from anomalies in the same season of the previous and following years, which leads to an effective sample size of 30 (number of years). For all spatial maps that show temporal correlation coefficients in shading for the different geographical regions, an absolute value of the correlation coefficient of ~0.31 is statistically significant at the 90% confidence level using a two-tailed t-test (df = 28). Thus, we are not showing any correlations below an absolute value of 0.3 (white shading) in these maps.

National-level data

Irrigated rice production in the Philippines has almost tripled over the past thirty years, while rainfed rice production has seen a much smaller growth ( Fig 1A ). Over this period, yields for both production systems have increased steadily ( S1A Fig ). Besides this long-term trend, annual rice yields at the national level have been fairly stable over this period, with irrigated paddy rice production having only six yield anomalies exceeding one standard deviation (absolute anomaly of 0.09 [t ha -1 ], which corresponds to ~2.5% of the annual long-term mean in irrigated), while rainfed upland rice crops exhibited eight yield anomalies exceeding one standard deviation (absolute value of 0.07 [t ha -1 ], which corresponds to ~2.9% of the annual long-term mean in rainfed; S1B Fig ). Relative anomalies in total rice production ( Fig 1B ) are larger than those in yield, implying that the effects of climate variability are compounded through both yield and harvested area changes. As a result of the frequent occurrence of natural disasters in the Philippines, production losses are often manageable and built into farm management [ 41 ]. Notable exceptions are 1998 –with two typhoons–and 2010 –with four typhoons, an earthquake and a flood–which both saw large negative production anomalies [ 42 ].

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A) Annual rice production in the Philippines; B) Annual rice production anomalies (with regard to a 7 yr moving average); C) Quarterly rice production; D) Normalized quarterly rice production anomalies (the annual cycle is removed and the anomalies are with regard to a 7 yr moving average); E) Normalized quarterly rice yield anomalies. Additionally, d) and e) show the quarterly normalized soil moisture anomalies averaged from 117°E-128°E and 4°N-22°N (black line) and the normalized Niño3.4 index (yellow line). In all panels, R indicates instantaneous correlation except for the correlation coefficients in D) and E) between rice production/yield and soil moisture, which are given for a 3 months lead time of soil moisture, between Niño3.4 and soil moisture for a 4 months Niño3.4 lead time, and between Niño3.4 and rice production/yield for a 7 months Niño3.4 lead time.

https://doi.org/10.1371/journal.pone.0201426.g001

Aggregating the yield and production data on an annual time scale potentially masks seasonal modulations of both the large-scale climate variability [ 24 ] and crop-climate relationships [ 25 ]. As a result, quarterly production and yield anomalies [ Fig 1D and 1E ] show more variability than the annual data. Rainfed and irrigated rice production anomalies are substantially less correlated with production in the quarterly data (R = 0.65, significant at the 99% confidence level with df = 28) than in the annual time series (R = 0.86, significant at the 99% confidence level with df = 28). About 10% of variance in anomalous rice production on the national level is related to soil moisture variability, which is strongly negatively correlated with the Niño3.4 index ( Fig 1D ). This reduction of rice production during El Niño events is qualitatively similar to the results of global analyses [ 26 ]. While the correlation coefficients between soil moisture anomalies and rice production anomalies are approximately the same for irrigated (R = 0.33, significant at the 90% confidence level with df = 28) and rainfed (R = 0.34, significant at the 90% confidence level with df = 28) rice production, when looking at yield anomalies the correlation is higher for rainfed than for irrigated systems ( Fig 1E ). This shows that, as expected, irrigation can counter much of the plant physiological response to soil moisture changes (as measured by rice yield), but decisions on planting area (as included in rice production) remain sensitive to water availability [ 25 ].

ENSO impacts on soil moisture

On a regional scale as well as on the national level, the correlation between the Niño3.4 index and soil moisture anomalies in the Philippines is negative ( Fig 2 ), i.e., El Niño events lead to dry conditions in all parts of the country. Interestingly, the correlation between ENSO and soil moisture decreases in the third and fourth quarters ( Fig 2 ). One factor might be that in the summer season rainfall variability is dominated by tropical cyclone activity [ 43 ]. While tropical cyclone activity can be modulated by large-scale climate variability such as ENSO, it can be considered a mostly stochastic process on climate timescales. This wet season (Quarters 3 and 4) is also the season when most rice is planted ( Fig 1C ), indicating that wet-season rice production may be largely decoupled from ENSO variability [ 25 ].

https://doi.org/10.1371/journal.pone.0201426.g002

Regional crop-climate relationships

Rice in the Philippines is in the field for 90–110 days, so that planting decisions are made about three months before harvest [ 25 ]. Looking at the lagged correlation between rice production and soil moisture (soil moisture leading by one quarter, Fig 3 ), in most seasons, soil moisture anomalies in the previous quarter are significantly correlated with production variability, with higher soil moisture usually associated with increased rice production. Locally, seasonal correlations can be much higher than the national-level data ( Fig 1D ). A notable exception to this is Quarter 4, when correlations between these two variables are small, or even negative ( Fig 3 ). Production in this quarter is the highest of the year ( Fig 1C ) and represents the wet-season crop. Mean soil moisture conditions during the preceding quarters are high, so that variability in soil moisture does not affect rice planting or yield that much, while the typhoons that often impact the summer season (Q2-Q3) can lead to detrimental flooding in these quarters [ 43 ]. This is in accordance with an analysis of Luzon Island in the Northern Philippines (eight of eighteen regions; [ 25 ]), an area where both mean production ( S2 Fig ) and mean yields ( S3 Fig ) are high.

The annual cycle is removed and production anomalies are with regard to a 7 yr moving average. The soil moisture data are area averaged for each political region corresponding to the rice production data.

https://doi.org/10.1371/journal.pone.0201426.g003

Total rice production in any given region is a function of the crop area harvested, the crop yield per unit area, and the number of crops harvested per year. Climate variability influences all of these variables. In Quarter 3, when correlations between soil moisture and total rice production are strongly positive in most regions ( Fig 3 ), there were few locations with significant correlations between previous-quarter soil moisture and rice yield ( Fig 4 ). This means that in this season, soil moisture anomalies might mostly drive planting decisions (i.e., which areas are brought into production), without strongly affecting plant development. During the dry season (Quarters 1 and 2) on the other hand, there are also significant regional correlations between soil moisture and rice yields, implying that climate variability in this season affects both plants and planting decisions. Mean climatological soil moisture conditions thus strongly affect the rice production response to climatic forcing. In contrast to soil moisture, in most regions temperature variability has a much lower correlation with rice yields. In some regions however, ENSO-induced temperature and precipitation changes have an effect in the same direction: El Niño events usually result in dry and hot conditions in the Philippines, which both are associated with a decrease in yield ( S4 Fig ). As we have seen, ENSO is driving a significant part of soil moisture variability in the Philippines which is correlated with rice production variability. Therefore, the predictive skill for ENSO that is seen in operational seasonal forecast models [ 44 ] up to several seasons ahead translates into important information for agriculture management in the Philippines and the possibility to mitigate some of the ENSO-induced effects on rice yields.

The annual cycle is removed and yield anomalies are with regard to a 7 yr moving average. The soil moisture data are area averaged for each political region corresponding to the rice yield data.

https://doi.org/10.1371/journal.pone.0201426.g004

The correlation between rice production anomalies in upland rainfed and lowland irrigated systems is stronger in annual data ( Fig 1B ) than in the quarterly data ( Fig 1D ). On a regional level, there is a differential response to climate forcing between these two different management systems. For rice yield in particular, the response to soil moisture changes is, not unexpectedly, stronger for rainfed than for irrigated crops ( Fig 4 ). Previous work found that on a global scale as well, yield losses during El Niño events are greater in rainfed areas compared to irrigated regions [ 26 ]. This shows that irrigation can provide a potentially useful management tool to mitigate climate impacts on rice production in the Philippines. At the same time soil moisture conditions are a direct proxy for local water availability–a major limiting factor for crop yield and production [ 45 ]–which could explain the correlations seen between irrigated rice yields and soil moisture anomalies in Quarter 2 ( Fig 4F ).

When looking at specific regions of high rice production ( S2 Fig ) on Luzon Island (large island in the northern Philippines that includes the regions Cagayan and Central Luzon) and Mimaropa (Southwestern islands within the Philippines), production and yield responses to soil moisture anomalies are not always consistent between these areas (Figs 3 & 4 ). Mimaropa exhibits one of the most consistently positive correlations between soil moisture anomalies and crop output in the Philippines, both in terms of total production and crop yield, and in rainfed and irrigated systems alike. In Central Luzon on the other hand, the response is more variable, and correlations are generally low for rice yields. Negative correlations between soil moisture and yield or production in some quarters and regions may reflect the damaging impact of flooding on rice, which happens fairly frequently [ 42 ]. Due to this nonlinear impact of rainfall on rice yield (i.e., an increase of rainfall can lead to either positive or negative rice yield depending on thresholds in the system), the actual yield variance explained by climate might be larger than suggested by linear correlation analysis, which should be explored further in future studies.

Sensitivity to climate in the future

As we have shown here, climate-induced rice production variability in the Philippines over the past three decades has mostly been related to soil moisture changes, which in turn were associated with large-scale inter-annual rainfall variability caused by the El Niño–Southern Oscillation. This is in line with previous studies that show that although rice is grown over a large environmental range in both temperate and tropical areas [ 46 ], more variance in yield in tropical areas is usually due to precipitation (and thus also soil moisture) rather than temperature. Generally, tropical environments have relatively small variability in temperature, so other factors such as solar radiation, precipitation, or soil nutrient availability have a larger impact on crop production [ 47 ]. However, this particular expression of crop sensitivity to large-scale climate may fundamentally change in a warming climate [ 48 ].

In the Philippines, temperatures year-round are currently within the range of favorable growing conditions for rice ( Fig 5 ). Despite the fact that we see a significant proportion of variance explained by ENSO-mediated soil moisture variability, in the future the effect of temperature is likely to become increasingly important: If greenhouse gas emissions continue unabated, by the end of the century summers in the Philippines will be warmer than during the historical record [ 12 ]. Fig 5 shows the year-to-year variability in present-day quarterly temperatures, and how this is projected to change with 2 and 4°C of global warming. Over the past century, quarterly temperatures averaged over the Philippines never exceeded 27°C. With 2°C of global warming, median quarterly temperatures would be outside of the present-day range. With 4°C of global warming, year-to-year temperature variability will be entirely above the range of present-day variability. The effects of this will be particularly impactful during the dry season in Quarter 2, when temperatures are already high and there is low capacity for mitigation through soil moisture.

The variance in future temperatures represents inter-model spread and present-day interannual variability. Occurrence points of rice in the tropics (23.5°S–23.5°N) using quarterly data are plotted in grey, with frequencies rescaled by a factor of 4. Rice location data were downloaded from Genesys PGR [ 78 ].

https://doi.org/10.1371/journal.pone.0201426.g005

Under business-as-usual emissions (RCP8.5), the global mean temperature is projected to increase by 2°C as early as 2042, with a median prediction of 2055, and by 4°C between 2075 and 2132. Even in an emissions scenario aiming to stabilize greenhouse gas concentrations by mid-21st century (RCP 4.5), global mean temperature could rise by 2°C as early as 2052 [ 40 ]. Based on the temperature projections for these global warming targets, the Philippines is thus likely to see a fundamental shift in the climate–rice relationship over the course of the next few decades. This analysis focuses only on seasonal-mean temperature projections. However, the average precipitation, inter-annual climate variability, and the frequency of extremes may change as well, but projections for these are much more uncertain.

Regional and quarterly data of climate variability and rice production in the Philippines show that ENSO-induced changes in soil moisture are a major source of climate-driven production variability, especially during the dry season. Wet-season soil moisture changes seem to be more stochastically driven, and therefore more independent from large-scale climate forcing such as ENSO. During this main growing season background soil moisture conditions are high, so factors other than climate drive planting decisions and crop yields. The sensitivity to climate variability is higher in upland rainfed systems than in lowland irrigated systems, and varies strongly by region.

Regional differences in crop-climate relationships could be partly explained by differences in soil type, which determine water-holding capacity and thus soil moisture content and cropping patterns. Other factors that contribute to regional differences include different rice variety choices, different management practices (fertilization, mechanization, planting date, post-harvest storage), as well as different market demands. Cropping calendars also differ across political regions, which creates a differential ability to respond to climate events (e.g., ENSO), accentuating seasonal differences and changing vulnerability. Predictions of ENSO conditions are skillful in the current generation of seasonal forecast models [ 44 ], which translates into information that can be utilized for agriculture management in the Philippines and provides a possibility to mitigate some of the effects of ENSO on rice yields and production. Importantly, extreme ENSO events (such as the 1997/98 El Niño) that lead to large disruptions of the tropical hydroclimate, are projected to occur more frequently by the end of the century in response to greenhouse gas forcing [ 49 ]. Thus, the dual calamity of projected changes of both the climate mean state and ENSO-induced hydroclimate variability will likely constitute significant challenges to future rice production in the Philippines.

Implications for food security in the Philippines

In any given year national production may be adequate, but there might be severe regional shortfalls that impact both food price and security. In the Philippines, regional shortfalls are evident in years when severe natural disasters occurred [ 42 ]. Regions of high mean production ( S2 Fig ) and yield ( S3 Fig ) naturally dominate the signal seen in national production and yield data ( Fig 1 & S1 Fig ). However, individual regions and provinces may experience food insecurity that differ from those seen at the national level and can potentially be more severe. The regional relationships between climate variability and production/yield in combination with both the regional long-term mean production/yield and seasonal climate forecasts might help to mitigate future impacts.

Food in the Philippines is relatively mobile, but food prices are more volatile in years with natural disasters [ 50 ] and yield shortfalls may disproportionately impact small holders [ 51 ]. These negative effects might be mitigated by changing land use patterns, production techniques, or germplasm (breeding material, crop types stored in gene banks, heirloom types, or wild relatives). In the past, land policies in the Philippines have favored expansion of production [ 52 ], focusing on increased planting of annual staples [ 53 ]. This has led to a steady increase in area under cultivation, including areas that were historically used for other crops. Further, domestic Philippine rice production has been incentivized [ 53 ]. As a result, rice yields increased ~1% a year during the second half of the 20 th century due to both management and genetics [ 54 ], while the area of rice production increased by 50% [ 16 ].

Nonetheless, the Philippines are a large importer of rice (~10% of marketed rice per year). This is due geography [ 55 ], international policy pressure [ 56 ], and colonial history [ 56 ], with imports increasing during times of stress (e.g., during the 1997/98 El Niño when rice imports tripled due to fewer harvestable hectares [ 16 ]). This has led to calls for self-sufficiency in rice production which, while possible, would be difficult to achieve with current agricultural policy in the Philippines [ 56 ] that can leave rice markets susceptible to price increases [ 20 ]. It is hypothesized that if there is renewed investment in agriculture, coupled with improved technology and skillful seasonal forecasting, imports could be reduced, helping to increase domestic food security. However, it is unclear if increased investments will provide the necessary buffer to the system to maintain production increases, especially in a changing climate. Additionally, there have been substantial efforts to breed drought resistant rice, with mixed results, due to the trait complexity [ 57 ], though new varieties show promise [ 58 ].

The north-central area of the Philippines is one of the longest continuously-cultivated areas of rice production in the world. Over time, the objectives of breeding and agronomic endeavors have changed, from local heirloom grown on terraces to mega-varieties grown in an industrial setting across millions of hectares [ 59 ]. At the moment, there is increasing interest in heirloom varieties with specific growth environments as a source of both food and export potential [ 60 ]. In subsistence settings, rice farming is supplemented by local trade economies that can increase local food security [ 61 ]. Moreover, there is a complex agricultural landscape established in the northern Philippines, specifically in Ifugao (rice terraces), where historic intensification has been accompanied by extensification [ 62 ]. These examples support the idea that the agro-cultural context can help mitigate the impacts of environmental pressure on food security.

The role of temperature variability

Our results indicate that temperature variability at present is not a big driver of rice production variability ( S4 Fig ). Under continued greenhouse gas emissions however, the range of temperature variability in the Philippines is projected to be outside the present-day envelope by the end of the century ( Fig 5 ). Increasing temperatures will have major implications for rice production in the Philippines. Recent work estimated that for every degree Celsius global temperature increase, global mean rice yields will decline by 3.2 ± 3.7% [ 63 ]. These reductions were projected without consideration of potential CO 2 fertilization, adaptation in agronomic practices, or genetic adaptation [ 63 ]. While a recent meta-analysis identified an increase in yields under increased CO 2 , this may not be an even increase across crops or regions [ 64 ]. Additionally, a comparison between historic and modern cultivars suggests that during modern breeding there has not been a selection for increased response to increased CO 2 concentrations [ 65 ], limiting the potential future CO 2 -fertilization effect.

The temperature sensitivity of crops is dependent on growth stage [ 66 ], time of day, and time of year, but generally a temperature increase of one degree can decrease yields by up to 10% once a temperature threshold is reached in rice [ 67 ; 68 ; 69 ; 70 ]. Due to this nonlinear threshold behavior, the relative importance of temperature variability to yield variability ( S4 Fig ) may increase in a warmer climate. The combined effects of high temperatures and moisture deficits could critically alter the seasonality and locality of the impact of ENSO on rice production. Furthermore, by the end of the century, inter-annual climate variability will regularly push climate in the Philippines outside the climatic range of current tropical gene accessions ( Fig 5 ). Most tropical rice accessions currently grow at quarterly temperatures below 28°C. In a 4°C warmer world, median quarterly temperatures will exceed this threshold year-round. In the second quarter in particular, temperatures will already regularly exceed 28°C with just 2°C of global warming. The performance of tropical rice crops in these climatic conditions has not been tested and is thus potentially a large threat to future food security.

Implications for plant breeding

The ability to increase yields under rising temperatures is a major target for plant breeders [ 71 ]. However, modern crop plants have undergone two significant population bottlenecks–the first during domestication and the second during improvement processes–that have resulted in a significant decrease of the crop’s genetic diversity relative to their wild progenitors [ 72 ]. For instance, modern Asian rice retains ~80% of the genetic diversity of its wild progenitor [ 73 ]. Generally, plant breeding involves crossing 'good by good', a strategy that results in a continuing loss of genetic diversity. Breeding targets focused on yield and quality have often left behind traits from landraces (heirloom lines that have not undergone modern breeding) and crop wild relatives [ 74 ]. Among these are many traits associated with tolerance to abiotic stress associated with climate change [ 75 ]. There have been increasing efforts to collect data surrounding landrace and wild material in germplasm collections (phenotypes, genotypes, biophysical, environmental) [ 74 ], which has led to the creation of a platform to understand the fastest and most practical way to bring in traits from landrace and wild crop material [ 76 ]. Breeding is a long-term endeavor, with a long research and development time [ 77 ]. This lag time requires a forward-looking approach in order to have plant material ready to be used in the field in time for projected changes in climate. By estimating the current and future temperature envelope of rice production in the Philippines, and comparing this to bioclimatic data of collection locations of rice accessions ( Fig 5 ), we have reduced the number of potential parents that could be used to breed for climate change, thus implementing the first stage of utilizing collections for breeding for climate change.

Conclusions

There is an increasing need to understand how climate variability will impact rice yields and production, particularly as human population continues to increase and climate changes. Comparing multiple spatial scales allows for a more complete understanding of what types of policy recommendations should be made, as it allows for a direct partitioning into the political units that are most likely to be effective at driving landscape change. This study identified ENSO as driving a significant part of soil moisture variability in the Philippines, which in turn is correlated with rice production and yield variability. Therefore, skillful seasonal predictions can provide useful information for agriculture management to mitigate climate-induced effects on rice production and yield. Future tropical climates is likely to be outside the range of optimal temperatures for rice production. This is true in the Philippines, and will likely require a modification of both genetics and agronomic practices. Detailed case studies like this will complement global yield impact studies and provide important local perspectives.

Supporting information

S1 fig. national-level rice yields in the philippines from 1987–2016: irrigated (blue) and rainfed (red) farming techniques..

The linear correlation coefficient R denotes the simultaneous correlation. a) Annual rice yield in the Philippines; b) annual rice yield anomalies (with regard to a 7 yr moving average); c) quarterly rice yield.

https://doi.org/10.1371/journal.pone.0201426.s001

S2 Fig. Long-term quarterly mean (1987–2016) rice production for both rainfed and irrigated systems.

Note that grid point values indicate the mean production value of the whole associated province.

https://doi.org/10.1371/journal.pone.0201426.s002

S3 Fig. Long-term quarterly mean (1987–2016) rice yield for both rainfed and irrigated systems.

https://doi.org/10.1371/journal.pone.0201426.s003

S4 Fig. Correlation coefficient R between and quarterly rice yield and surface temperature anomalies in the previous quarter.

The annual cycle is removed and yield anomalies are with regard to a 7 yr moving average. The temperature data are area averaged for each political region corresponding to the rice yield data.

https://doi.org/10.1371/journal.pone.0201426.s004

S1 Table. The table shows if rice is planted or harvested in the administrative regions of the Philippines according the PhilRice planting calendar.

https://doi.org/10.1371/journal.pone.0201426.s005

Acknowledgments

The authors thank Axel Timmermann, Stephen Acabado, and two anonymous reviewers for their valuable comments.

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Climate variability impacts on rice production in the Philippines

• Source: PLOS ONE
• Alternative Title: Climate variability impacts on rice production in the Philippines
• Journal Title: PLOS ONE
• NOAA Program & Office: OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) Less -
• Description: Changes in crop yield and production over time are driven by a combination of genetics, agronomics, and climate. Disentangling the role of these various influences helps us understand the capacity of agriculture to adapt to change. Here we explore the impact of climate variability on rice yield and production in the Philippines from 1987-2016 in both irrigated and rainfed production systems at various scales. Over this period, rice production is affected by variations in soil moisture, which are largely driven by the El Nino-Southern Oscillation (ENSO). We found that the climate impacts on rice production are strongly seasonally modulated and differ considerably by region. As expected, rainfed upland rice production systems are more sensitive to soil moisture variability than irrigated paddy rice. About 10% of the variance in rice production anomalies on the national level co-varies with soil moisture changes, which in turn are strongly negatively correlated with an index capturing ENSO variability. Our results show that while temperature variability is of limited importance in the Philippines today, future climate projections suggest that by the end of the century, temperatures might regularly exceed known limits to rice production if warming continues unabated. Therefore, skillful seasonal prediction will likely become increasingly crucial to provide the necessary information to guide agriculture management to mitigate the compounding impacts of soil moisture variability and temperature stress. Detailed case studies like this complement global yield studies and provide important local perspectives that can help in food policy decisions. More ▼ -->
• Keywords: [+] Agriculture Crop Yields El Niño Current Food Intensification Ocean Currents Prediction Quality Relative Biological Effectiveness Science Security Soils Southern Oscillation Stress Tolerance Technology Temperature
• DOI: https://doi.org/10.1371/journal.pone.0201426
• Pubmed Central ID: PMC6084865
• Document Type: Journal Article
• Place as Subject: Philippines
• Rights Information: CC BY
• Compliance: PMC
• Main Document Checksum: [+] urn:sha256:d0793d77a4e9933112e352e159c804992ff6804f25eed7619b2976f806600a0b
• Download URL: https://repository.library.noaa.gov/view/noaa/24470/noaa_24470_DS1.pdf

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Enhancing government services to rice farmers in the philippines: a service quality–sustainability-focused approach for long-term agricultural resilience.

1. Introduction

2. literature review, 2.1. theoretical research framework, 2.2. hypothesis development, 3. methodology, 3.1. participants, 3.2. questionnaire, justification for multiple measures, 3.3. structural equation modeling, 5. discussion, 5.1. theoretical implications, 5.2. managerial implications, 6. conclusions, 7. limitations and future research, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Ituriaga, J.; Mariñas, K.A.; Saflor, C.S. Enhancing Government Services to Rice Farmers in the Philippines: A Service Quality–Sustainability-Focused Approach for Long-Term Agricultural Resilience. Sustainability 2024 , 16 , 8108. https://doi.org/10.3390/su16188108

Ituriaga J, Mariñas KA, Saflor CS. Enhancing Government Services to Rice Farmers in the Philippines: A Service Quality–Sustainability-Focused Approach for Long-Term Agricultural Resilience. Sustainability . 2024; 16(18):8108. https://doi.org/10.3390/su16188108

Ituriaga, Jenel, Klint Allen Mariñas, and Charmine Sheena Saflor. 2024. "Enhancing Government Services to Rice Farmers in the Philippines: A Service Quality–Sustainability-Focused Approach for Long-Term Agricultural Resilience" Sustainability 16, no. 18: 8108. https://doi.org/10.3390/su16188108

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The Central Luzon Loop Survey: Rice Farming in the Philippines from 1966 to 2021

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• Kei Kajisa 9 ,
• Piedad Moya 10 &
• Fe Gascon 11  

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The Central Luzon Loop Survey in the Philippines is one of the longest-running and ongoing household-level farm surveys in tropical Asia. This chapter reviews the changes in rice farming from 1966 to 2021, with a particular focus on the past decade. The data show that rice yields have stagnated and become more variable despite a prompt and continuous switch to newer modern varieties with an appropriate nitrogen application level since the Green Revolution. This implies that the Green Revolution-type agricultural development is at a crossroads. As background factors, this chapter reviews how the adoption of labor-saving technologies, mechanization, and farm size have changed over time under increasing rural labor scarcity. A subjective assessment of the impact of COVID-19 on rice farming is also discussed.

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The Evolution of Rice Farming in the Lower Mekong Basin

Introduction: Why an African Green Revolution Is Needed and Why It Must Include Small Farms

On the determinants of low productivity of rice farming in mozambique: pathways to intensification, 1 introduction.

The Central Luzon Loop Survey in the Philippines (the Loop Survey) is one of the longest-running and still ongoing household-level farm surveys in tropical Asia. Footnote 1 The International Rice Research Institute (IRRI) started the survey on the eve of the Green Revolution in 1966, the year of the official release of the miracle rice, IR8. Since then, the survey has been conducted every four to five years until 2021, generating 14 rounds of datasets, covering a period of more than half a century. Footnote 2 The datasets of this feature enable us to explore the situation of rice farming and rice farm families before the Green Revolution, how the situation changed through the progress of the Green Revolution, and the emerging issues in the post-Green Revolution era in the Philippines.

The Loop Survey revealed Green Revolution’s substantial impact on the country’s food production and poverty alleviation. Among the 34 major publications (books, reports, and journal articles) produced from the Loop Survey, comprehensive documentation from 1966 (first round) to 2012 (12th round) was found in a study by Moya et al. ( 2015 ). Footnote 3 It shows that the paddy (unmilled rice) yield per hectare had increased from 2.3 tons per hectare (t/ha) in 1966 to 3.9 t/ha in 2011 in the wet (rainy) season and from 1.8 t/ha in 1967 to 5.8 t/ha in 2012 in the dry season, thereby increasing the farmers’ rice income. Accordingly, the first-generation Green Revolution farmers increased schooling investment in their children, resulting in an increase in the proportion of secondary- or tertiary-level graduates from 18 to 65% in the same period. These educated children moved to the non-agricultural sector. Hence, although the proportion of rice income increased from 68% in the 1960s to 86% in the 1970s, it decreased successively since then to the level of 17% in the first decade of the twenty-first century (‘00s), whereas the proportion of off-farm income and remittances accounted for 34% and 28%, respectively. In general, countries benefiting from the Green Revolution show a similar pattern of agricultural development and income change (Otsuka et al. 2008 ).

However, Green Revolution-style agricultural development is now at a crossroads. It is ironic that the Green Revolution, which has achieved success through the advancement of seed-fertilizer technology and the adoption of labor-intensive crop care, is now challenged by increasing rural labor scarcity caused by its success. This is an inevitable historical pattern of agricultural transformation in the Philippines and other countries that have started economic ‘take-offs’ (Viswanathan et al. 2012 ; Briones and Felipe 2013 ; Timmer 1988 ). Furthermore, disasters and infectious disease pandemics are becoming increasingly rampant as contemporary phenomena. The achievement of sustainable rice farming is challenged by these contemporary issues.

This chapter aims to identify emerging issues on rice farming in the post-Green Revolution era in the Philippines using the last two rounds of the Loop Survey, namely the 2015–16 and 2020–21 rounds. This discussion includes the impact of the COVID-19 pandemic on rice farming.

2 Survey Design and Survey Site

The use of ‘loop’ in the name stems from the survey’s sampling feature: selecting sample farm fields along the loop of the national highway passing through six provinces (Fig. 3.1 ). Randomization of the sample was achieved by specifying the fields to be observed at specific kilometer posts along the main highway (e.g., the 50th, 60th, 70th, etc.). The most important feature of the data is that they were collected from the same fields despite changing operators. Hence, this dataset provides long-term, plot-level panel data. The initial sample size was 95 farmers who cultivated 120 parcels in 1966, gradually decreasing mainly due to land conversion to non-agricultural purposes, thus supplemented in the 1979–80 round, for a total of 148 farmers with 338 parcels. Since then, no compensation has been made, resulting in a sample size of 81, with 126 parcels in the 2021 interview. The sample size for each round is presented in Table 3.1 .

Map of the Central Luzon Loop Survey

The area is known as the country’s rice bowl and has a distinct wet season (WS) and dry season (DS)—the WS begins in May or June and ends in October, and the DS begins in November and ends in March or April. The introduction of large-scale surface irrigation systems in the 1970s and the adoption of low-lift pumps and shallow tube wells in the 1990s have made DS rice farming possible. Footnote 4 Accordingly, the crop intensity (taking a value of 2 if fully double-cropped), which was 1.33 in 1966–67, jumped up to 1.55 in 1979–80 due to the availability of surface irrigation systems, and then further increased to 1.82 by 2011–12, mainly due to the expansion of pump irrigation.

The last feature of the survey site is land ownership and tenure distribution. Large rice and sugarcane haciendas (plantations) developed in this area during the Spanish colonial period in the nineteenth century. Given this historical background, the Central Luzon region was targeted as the first place for implementing the comprehensive land reform program. Footnote 5 From 1966 to 2012, the distribution of tenancy changed from 13 to 47% as owners, 13–29% as leaseholders, 75–5% as share tenants, and 0–19% as borrowers, indicating an increase in owner or leaseholder cultivators who used to be the share tenants. Usually, in other countries, land reforms are implemented at once in a short period, but it is unique in the Philippines that the program has been continuously extended, and the reform is continuing (as of 2021).

The last survey round was conducted under the COVID-19 pandemic using telephone interviews one year after the regular cycle. Hence, it covers the regular period of 2019–20 with recall data and the period of 2020–21. In the telephone interviews, questions were limited to key variables, but they also included questions about the impact of the COVID-19 pandemic. This chapter used only 2020–21 data as the data patterns in 2019–20 are quite similar.

3 Recent Changes

The Asian rice Green Revolution has been led by farmers’ vigorous adoption of seed-fertilizer technology under irrigated or favorable rainfed conditions. Figure 3.2 shows the seasonal diffusion of modern varieties (MVs) from 1966 to 2021. Following the analytical style of Estudillo and Otsuka ( 2001 , 2006 ) and Laborte et al. ( 2015 ), the varieties are classified by generation based on their release dates and distinct characteristics, consisting of the traditional variety, the five modern variety generations (MV1 to MV5), and hybrid rice. Footnote 6 The figure indicates that the switch from old to new MVs has occurred promptly; more than 70–80% were replaced within four-year intervals. This implies that Loop farmers are active farm managers with strong enthusiasm for newer technologies. Recently, hybrid rice varieties have become popular, particularly in the DS when the risks of pests, diseases, and harsh weather shocks are low under irrigated conditions (Laborte et al. 2015 ). The hybrid varieties have a potential yield of approximately 10–14 t/ha compared with 6–10 t/ha of the latest inbred varieties. This has proceeded since 2011, and 7% of farmers in the 2020 WS and 24% in the 2021 DS cultivated the hybrid varieties.

Trends in the adoption of modern varieties in the Wet season (left) and the Dry season (right), 1966–2021 (The Loop Survey)

In parallel with MV diffusion, farmers increased the application of inorganic fertilizers. The Loop data indicate that the amount of nitrogen applied to rice fields started at 9 kg per hectare (kg/ha) in 1966 (pre-Green Revolution), increased steadily since then, and in the 1987 DS and the 1994 WS reached close to the recommended 100 kg/ha level (Moya et al. 2015 ). The nitrogen application level has been approximately 100 kg/ha since then.

What is the impact of the diffusion of seed-fertilizer technology on rice productivity? Figure 3.3 shows the long-term trend of the mean (Panel A) and coefficient of variation (CV) (Panel B) of the paddy yield (kg/ha). The yield increased sharply during the early phase of the Green Revolution (the 1970s and the 1980s). During this period, the CV increased initially in the 1970s but steadily declined until the early ‘00s, indicating that the Green Revolution technologies were much riskier than the traditional ones when they were introduced, but gradually standardized.

Trends in the mean (left) and the coefficient of variation (right) of paddy yield, 1966–2021

We can identify two features in the recent rounds: (1) stagnant yield growth in the WS since the late 1990s and in the DS since the 2010s, and (2) the increasing trend of the CV since the 2010s. As we have seen, the adoption of hybrid rice varieties has continued since 2011. However, the yield did not significantly increase. The recent trend indicates that the potential yield has not been fully realized in the fields and that the stability of rice production has been diminished.

This trend may be attributed to two major reasons. First, many sources indicate that natural disaster events, such as floods and insect outbreaks, are increasing in the Philippines, but the varieties commonly planted in recent years (i.e., MV4 and MV5) are characterized by lower resistance to pests and diseases compared to MV2 and MV3 (Laborte et al. 2015 ). In addition, floods have become more rampant in Central Luzon because newly-constructed factories and roads block water flow to the drainage. In this regard, natural and human-made disasters have hindered yield increases in this region. Second, increasing labor shortages require a structural transformation in rice farming, but this has not been fully achieved. The second point is discussed later in this section.

How has the increasing labor shortage affected rice farming in this area? Table 3.1 shows the trend in the adoption of labor-saving technologies, farm size (operational landholdings including rented-in parcels and excluding rented-out parcels), and the area planted with rice from 1966 to 2021, revealing four features. First, small-scale mechanization proceeded rapidly after the Green Revolution and was completed in the early 1990s. The adoption rate of power tillers (hand tractors) and small threshers reached approximately 100% by the early 1990s.

Second, the adoption of combine harvesters has jumped up in the last two rounds—the government promoted it as a replacement for manual harvesting. Its utilization increased in both seasons from 0% in 2011–12 to 96% in 2020–21. This was the reason for the sharp decline in the use of small threshers to the level of 3% in the 2020 WS and 8% in the 2021 DS.

Third, crop establishment still fully relies on manual labor—it can be done either through transplanting or direct seeding, with the latter—broadcasting seeds directly on a field—being a labor-saving method introduced in this area in the 1980s. However, it is appropriate only for plots with suitable water control because otherwise, the germination of seeds cannot be synchronized. Hence, as shown in Table 3.1 , the boom in direct seeding’s adoption during the introduction period notwithstanding, particularly in the WS when water control is more difficult; the adoption rate in the WS decreased to merely 7% in 2008. However, the last round survey shows it increased again to 27% in 2020, presumably reflecting increasing challenges in finding a sufficient number of laborers for transplanting. Simultaneously, transplanting machines have not been used in the 2020–21 round. Thus, crop establishment is still a relatively labor-intensive activity, although not as much as in the past when direct seeding technology was unavailable.

Fourth, farm size (shown in the lower part of the table) shows no dramatic change at approximately 2 hectares (ha). Given this farm size, the area planted with rice in the WS declined from approximately 2 ha in the 1970s to approximately 1 ha in the 1980s. It then remained almost unchanged at slightly more than 1 ha. In contrast, the area in the DS was slightly less than 1.5 ha throughout the survey period. To better understand this aspect, we need to consider the land reform issues of this country. The land reform program has continued to be extended, and there is concern that landlords are reluctant to rent out their land for fear of land expropriation, resulting in an inactive land rental market. This could be a hurdle for land consolidation and further progress in large-scale mechanization.

In summary, mechanization is still limited to land preparation, harvesting, and threshing, and enlargement of the farm size has not been realized at the study site. In other words, the agricultural transformation has reached only the halfway mark.

As explained above, crop establishment depends fully on manual labor as of 2020–21. Nevertheless, the labor employment for this activity is also affected by the increasing rural labor shortage. Table 3.2 shows the number and composition of hired labor for crop establishment by labor type from 2012 to 2016 using the recall data collected in the 2015–16 round. We classify hired labor into three categories based on the length of the working period: (1) regular workers who have worked for the interviewee farmer for more than five years in total; (2) occasional workers who have worked for 1–4 years in total; and (3) new workers who worked for the first time. Footnote 7

The table clearly indicates that it had become more challenging to recruit regular workers, and the farmers had to rely more on new workers in both the WS and the DS. The proportion of regular workers decreased from 62 to 35%, whereas that of new workers increased from 17 to 28% in the WS. A similar trend was observed for the DS. The stagnant and fluctuating yield in recent rounds may stem from the management challenges of new unknown laborers who might not only be unfamiliar with the agronomic characteristics of hiring farmers’ particular plots (thus cannot do transplanting efficiently) but also be less reluctant to commit opportunistic behaviors, such as the delay or absence in the appointment and labor effort shirking. Footnote 8

Therefore, Table 3.2 implies that although labor was becoming scarce within the Loop villages, it was still available from distant areas at least until the 2015–16 round. As the Ricardian trap model predicted, the labor wage rate would not rise if this were the case. Figure 3.4 shows the agricultural wage rate trend from 1966 to 2021. It clearly shows that although the nominal wage rate continued to increase sharply, particularly after the 1980s, the real wage rate (deflated by consumer price index [CPI] or paddy price) initially increased from the 1980s until the mid-1990s but was relatively stable in the ‘00s until the 2015–16 round. However, the real wage rate seems to have started to rise in the 2020–21 round. A sharp increase in the real wage rate in the 1980s is puzzling because economic growth was slow, and population growth was high during that period.

Trends in agricultural labor wage rate, 1966–2021 (Loop Survey for wage and paddy price; Philippine Statistics Authority for CPI)

Last, we provide an overview of the impact of the COVID-19 pandemic on rice farming. In our survey period, the 2000 WS and the 2021 DS were the pandemic periods of the country, with a much higher number of cases in the 2021 DS. Anecdotal evidence indicates that, as possible negative effects, external labor activities were restricted, and input and output supply chains had limited activities. Meanwhile, many urban factory workers returned to their rural home villages because of the suspension of factory operations, which might have relaxed labor shortages.

Table 3.3 summarizes the subjective assessments of the aforementioned impacts. Contrary to our initial expectation, less than 15% of the farmers experienced challenges working outside and finding hired labor. Also, only approximately 20% claimed challenges in finding buyers for their harvest. Similarly, only 8% of farmers in the WS and 4% in the DS had challenges accessing chemicals and seeds, whereas 57% and 82% complained of increases in the prices of inputs in the WS and the DS, respectively. Regarding positive effects, approximately 10–20% of farmers recognized an increase in family or hired labor availability. These snapshots indicate that while the pandemic generated an enormous impact on the entire society, its effect on rice farming is limited, seemingly implying relatively stronger resilience of rural livelihoods.

4 Conclusion

Rice farming in Central Luzon is at a crossroads. Rice yields have stagnated and have become more variable in the last decade, despite a prompt and continuous switch to newer MVs. We discussed the adoption of labor-saving technologies and mechanization, stagnation of land consolidation and enlargement, increasing labor management challenges, and more rampant natural and human-made disasters. To choose the right direction moving forward from the crossroads, we need further studies to make rice farming more resilient to rapid demographic changes, rampant disasters, and future pandemics. The Loop Survey can provide important information for this purpose and contribute to drawing useful lessons for Asian countries and show possible future paths for rice-producing Sub-Saharan African countries.

Recollections of Professor Keijiro Otsuka

It is a great asset to my research life that I served as a researcher at IRRI from 2006 to 2012, where Professor Otsuka also served in the 1980s. There I learned the importance of fieldwork and interaction with researchers in other fields. – Kei Kajisa

It was an honor for me to know and interact with Professor Kei Otsuka when he was then a Senior Staff and Chairman of the Board of Trustees of IRRI. I learned a lot from his insights and knowledge of IRRI's research and management and how he was instrumental in securing stable funding during his term. – Piedad Moya

Professor Otsuka joined IRRI in the mid-1980s, and I was then a research assistant involved in his projects on how technological changes in rice farming affected farmers’ socioeconomic conditions in different areas in the Philippines. It was a great learning experience to pick up his approaches to collecting field information. I am greatly honored and privileged to have worked with a well-known economist and one who has a passion for sharing his research knowledge and experiences in his field of expertise. – Fe Gascon

Other distinguished long-term farm household surveys covering multiple villages include the Village Dynamics in South Asia (VDSA) by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), and the Bangladesh Panel initiated by IRRI and succeeded by the Bangladesh Institute of Development Studies (BIDS) and BRAC. Single village, fixed-point long-term surveys include the East Laguna village survey in the Philippines (Hayami and Kikuchi 2000 ) and Palanpur in India (Bliss and Stern 1982 ; Lanjow and Stern 1998 ; Himanshu et al. 2018 ).

See Appendix Table 3.4 for the researchers involved in each round. Keijiro Otsuka led the 6th round (1986–87).

See Appendix B of Moya et al. ( 2015 ) for the 33 publications (other than Moya et al. 2015 ) released by 2009.

The completion of the Pantabangan Dam in 1975 and the establishment of the Upper Pampanga Integrated Irrigation System represented the first major irrigation project in the region. The Casecnan Irrigation and Hydroelectric Plant, which commenced in 2002, diverts water from the Casecnan and Taan rivers of Nueva Vizcaya to the Pantabangan Reservoir, further enhancing the expansion of the irrigated area in the region. In the last two decades, the adoption of low-lift pumps and shallow tube wells has been the major source of irrigation expansion, particularly in the dry season.

The Agricultural Land Reform Code (RA 3844), was a major advancement of land reform in the Philippines. It was enacted in 1963 to abolish tenancy and establish a leasehold system in which farmers paid fixed rentals to landlords, rather than a percentage of the harvest. In September 1972, the second presidential decree that Marcos issued under martial law declared the entire Philippines a land reform area. A month later, he issued Presidential Decree No. 27, which had the specifics of his land reform program. The reform attempted to convert share tenants to leaseholders when the landlord owned less than 7 hectares (ha) of land or to amortizing owners when the landlord owned more than 7 ha of land. The reform procedure involved two steps. The first, Operation Leasehold, converted share tenancy to leasehold tenancy with rent fixed at a rate of 25% of the average harvest for the three normal years preceding the operation. The second step, Operation Land Transfer, transferred land ownership to tenants. In the latter operation, the government expropriated the area in excess of the landlord retention limit, with compensation to the landlord being 10% of the land value in cash and the rest in interest-free redeemable Land Bank bonds. The land was resold to the tenants for annual mortgage payments over 25 years, and they were granted a Certificate of Land Transfer (CLT). Upon completion of the mortgage payments, the CLT holders were given Emancipation Patents (EP) on the land, that is, a land ownership title with the restricted right of land sale. In 1988, the Comprehensive Agrarian Reform Program (CARP), which covers non-rice and non-corn areas, was introduced and has been continuously extended (as of 2021). See Moya et al. ( 2015 ) for more details.

The MV1 is the first generation of modern varieties released from the mid-1960s to the mid-1970s, including IR8, sharing the trait of being high-yielding without pest and disease resistance. MV2 varieties released from the mid-1970s to the mid-1980s, were characterized as having short maturity with multiple pest and disease resistance traits. MV3 varieties released from the mid-1980s to the mid-1990s, added better grain quality, and a stronger host plant resistance trait, and MV4 (from the mid-1990s to 2005) added tolerance to abiotic stresses and lower amylose content (for soft-cooked rice) but had lower resistance to pests and diseases. MV5 varieties were released after 2005 without taking into account the difference in characteristics with MV4.

In our survey module, we also asked questions about where the laborers came from (for example, the same village, different village but still in the same municipality, and different municipality). Since we find that the location and the length of work period are highly correlated so that the workers from the distant locations are relatively newer than the others, we use only the length of work period for our analysis.

When farmers need many laborers for transplanting and manual harvesting, they usually call for a foreman, called a kabisiliya , who has his or her group of laborers. Hence, the control of opportunistic behavior is an issue that has to be handled by the kabisiliya. Anecdotal evidence during the interview tells that farmers are becoming more serious about finding a reliable kabisiliya.

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Kajisa, K., Moya, P., Gascon, F. (2023). The Central Luzon Loop Survey: Rice Farming in the Philippines from 1966 to 2021. In: Estudillo, J.P., Kijima, Y., Sonobe, T. (eds) Agricultural Development in Asia and Africa. Emerging-Economy State and International Policy Studies. Springer, Singapore. https://doi.org/10.1007/978-981-19-5542-6_3

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Increasing productivity of rice farms in the Philippines remains to be of great concern despite the heavy investments in the industry. The impact of Rice Tarrification Law is yet to be studied and also how the Rice Enhancement Competitiveness Fund (RCEF) can help depressed rice farms. If skyrocketing price of farm inputs is inevitable and price stabilization policies on rice is not enough, focus should then be made on making sure there is available lower-interest loans to qualified and poor rice farmers as suggested by both OLS and SUR estimates across most Regions under study. In addition, farm laborers should be trained and equipped as well on modern farm technologies and machineries. However, the effect of hired labor on rice yield should be evaluated and investigated further due to its negative estimates.

Self-sufficiency is the objective of every agricultural country. This study determined the self-sufficiency in rice status of the Philippines by analyzing the fourteen rice-producing regions in the Philippines from 1992 to 2012. The researcher raised three hypotheses. Results of the analysis showed rejection of these three hypotheses implying that (1) Input factors affect production, consumption, and importation; (2) CAR and Central Visayas are the two most productive and efficient regions; and (3) Rice production and rice importation affect rice selfsufficiency positively, while rice consumption affects rice self-sufficiency negatively. Overall, the Philippines is not rice self-sufficient due to increasing gap between production and consumption.

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Climate variability impacts on rice production in the Philippines

Malte f. stuecker.

1 Center for Climate Physics, Institute for Basic Science (IBS), Busan, Republic of Korea

2 Pusan National University, Busan, Republic of Korea

Michelle Tigchelaar

3 Department of Atmospheric Sciences, University of Washington, Seattle, WA, United States of America

Michael B. Kantar

4 Department of Tropical Plant & Soil Sciences, University of Hawaii at Manoa, Honolulu, HI, United States of America

Associated Data

Data was sourced from the following third party providers: Rice production data from 1987-2016 were obtained from the Philippine government statistic authority ( http://countrystat.psa.gov.ph/ ). ENSO variability was characterized using the Niño3.4 (N3.4) index, which is calculated as the area averaged sea surface temperature anomalies from HadISST1 ( https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html ) in the region 170°W-120°W and 5°S-5°N. Soil moisture data were obtained from CPC (version 2) at 0.5º horizontal resolution (35) ( https://www.esrl.noaa.gov/psd/data/gridded/data.cpcsoil.html ). Surface air temperature (2m) was obtained from the ERA-Interim reanalysis on a 0.125º horizontal grid ( https://www.ecmwf.int/ ). Future climate projection data were obtained from the CMIP5 database for the business-as-usual scenario RCP 8.5 ( https://cmip.llnl.gov/cmip5/data_portal.html ).

Changes in crop yield and production over time are driven by a combination of genetics, agronomics, and climate. Disentangling the role of these various influences helps us understand the capacity of agriculture to adapt to change. Here we explore the impact of climate variability on rice yield and production in the Philippines from 1987–2016 in both irrigated and rainfed production systems at various scales. Over this period, rice production is affected by variations in soil moisture, which are largely driven by the El Niño–Southern Oscillation (ENSO). We found that the climate impacts on rice production are strongly seasonally modulated and differ considerably by region. As expected, rainfed upland rice production systems are more sensitive to soil moisture variability than irrigated paddy rice. About 10% of the variance in rice production anomalies on the national level co-varies with soil moisture changes, which in turn are strongly negatively correlated with an index capturing ENSO variability. Our results show that while temperature variability is of limited importance in the Philippines today, future climate projections suggest that by the end of the century, temperatures might regularly exceed known limits to rice production if warming continues unabated. Therefore, skillful seasonal prediction will likely become increasingly crucial to provide the necessary information to guide agriculture management to mitigate the compounding impacts of soil moisture variability and temperature stress. Detailed case studies like this complement global yield studies and provide important local perspectives that can help in food policy decisions.

Introduction

Rice–which provides nearly half the calories for half the world’s population [ 1 ; 2 ]–is a key crop for the Philippines: it is a staple food (with >110 kg/person/year consumption, [ 3 ], http://irri.org/rice-today/nourishing-a-nation ), the sixth highest per capita consumption in the world), as well as a major source of income (rice production valued at ~6 billion U.S. dollars in 2015; [ 4 ]). The Philippines produces approximately 3% of the world’s rice in both “lowland” flooded transplanted paddies and “upland” rainfed direct seeded areas [ 5 ]. As such, understanding what drives changes in rice production in the Philippines is essential for meeting current and future food security [ 6 ; 7 ]. Variations in crop yields can be explained by either endogenous drivers, such as genetics (including breeding methods–pure line, synthetic, hybrid) and agronomy (including technology–use of fertilizer, irrigation, machinery) [ 6 ; 8 ; 9 ], and exogenous forcing such as climate variability, which has been reported to decrease the influence of genetics [ 10 ]. The role of climate is becoming increasingly important due to anthropogenic climate change, which could drastically change local environments, damage yields [ 11 ; 12 ], and influence the yield stability of staple crops [ 13 ; 14 ]. Here we assess how current and future climate variability influences the various modes of rice production in the Philippines.

Continuing to feed a growing world population expected to reach ~9 billion by 2050 [ 15 ] while faced with a changing climate is a tremendous challenge. To date, global food production has steadily increased through innovations in agricultural technology (improved practices and genetics). The Philippines has mirrored global trends, with population increasing from ~26 million in 1960 to ~101 million in 2015, and rice production increasing from ~3.9 million tonnes in 1961 to ~19.0 million tonnes in 2014. This large improvement has been due to increased yields (production per unit area) and increased acreage being placed into production [ 16 ]. However, it is unclear whether it will be possible to sustain increasing production into the future [ 17 ], and if the changing land use patterns for agriculture are sustainable [ 18 ].

The Philippines is a large and spatially heterogeneous country, consisting of 7107 islands divided into 18 political regions and 81 provinces. There are four major climate regimes: 1) distinct wet monsoon and dry season, 2) no distinct dry season but a strong wet monsoon season, 3) intermediate between type 1 and 2, where there is a short wet monsoon and short dry season, and 4) an even distribution of rainfall throughout the year [ 19 ]. Planting dates vary between regions based largely on differences in climate ( S1 Table ). While rice in the Philippines is grown throughout the year ( S1 Table ), the largest production share is grown during the wet season. Due to this diversity of planting and harvesting, the government of the Philippines takes annual, semester, and quarterly statistics on rice production and harvested areas. Farms in the Philippines are generally small (less than two hectares on average; [ 20 ]), which may limit the implementation of advanced farming technologies. Currently, irrigated paddy rice accounts for 60% of total production [ 21 ], with the remainder grown as upland directly seeded rice.

In the Philippines, the dominant climate influence on inter-annual timescales is from the El Niño–Southern Oscillation (ENSO). ENSO has pronounced effects on global rainfall and temperature variability, particularly in the Indo-Pacific region [ 22 ; 23 ; 24 ]. It has been shown that this inter-annual climate variability can drastically impact crop yields and production globally [ 14 ; 25 ; 26 ]. In the tropical western Pacific region, El Niño events (the warm phase of ENSO) generally have a negative effect on farming. Specifically, El Niño induced droughts in the western Pacific have detrimentally affected Indonesian rice production [ 27 ], with worsening effects projected in response to greenhouse gas forcing [ 28 ]. Previous work on ENSO in the Philippines has shown that dry-season rice production is negatively impacted by El Niño on Luzon Island [ 25 ]. Additionally, tropical cyclones are a source of weather variability that is strongly seasonally modulated and exhibits localized impacts, suggesting that climate-yield and climate-production relationships need to be evaluated regionally and on sub-annual timescales.

An important limiting factor to increased food production in response to population growth and dietary shifts in the next century is the ability of crops to respond to climate variability, for instance soil moisture, surface temperatures, and the frequency of severe storms [ 29 ; 30 ]. Studies of climate impacts on crops typically either use process-based crop models, or evaluate the statistical relationship between crop production and climate variability in the past. Here we use this latter method to evaluate the impact of climate variability on rice production in the Philippines in different spatial and temporal contexts, and compare the range of past climate variability to projected future climate change to assess whether these relationships can be expected to hold in the future. We find that using a finer temporal and spatial resolution provides a more detailed understanding of climatic drivers of rice production, especially for upland (rainfed) rice, which is significantly impacted by ENSO through modifications in soil moisture. By the end of the century, temperatures will likely exceed present-day ranges, and will thus become an additional limiting factor to rice yield and production.

Materials and methods

Data acquisition.

Rice production data from 1987–2016 were obtained from the Philippine government statistic authority ( http://countrystat.psa.gov.ph/ ) for each political region and nationwide. Area harvested (hectares) and production (metric tonnes) data were collected from each political region and for the whole country for each quarter and year, for both irrigated and rainfed rice production. Missing data (where survey data was not complete) were linearly interpolated for each region (harvested area and production for rainfed systems) on the quarterly data (less than 1% of the data were missing). No values were missing for irrigated systems. Yield (tonnes per hectare) was calculated by dividing production by area for each quarter from 1987–2016.

To explore the ecological tolerance of rice we obtained the locality information of accessions stored in gene banks worldwide from https://www.genesys-pgr.org for tropical localities (from 23.5°S-23.5°N). From geo-referenced coordinates, we obtained surface temperature data for tropical rice from the WorldClim database at 30 arc seconds resolution [ 31 ], which were used to explore the climatic space inhabited by tropical rice.

Yield normalization

We created continuous time series of production and yield (rainfed and irrigated) for each aggregated political region. To remove the effect of yield increases due to breeding methods, we removed a ~7 year (27 quarters) running mean from each continuous time series and afterwards removed the residual total mean to construct an anomalous time series with zero mean. The results were qualitatively stable to the choice of the running mean window size (a 5 year window was also tested, data not shown). These normalization timescales are commonly used in the literature [ 11 ; 32 ] and correspond to a normal life cycle of a rice genotype used in farming [ 33 ].

Climate data

To calculate climate anomalies, we removed both the annual cycle (1987–2016 climatology) and the linear trend from each of the climate variables used. ENSO variability was characterized using the Niño3.4 (N3.4) index, which is calculated as the area averaged sea surface temperature anomalies from HadISST1 [ 34 ] in the region 170°W-120°W and 5°S-5°N. Soil moisture data were obtained from CPC (version 2) at 0.5° horizontal resolution [ 35 ]. Surface air temperature (2m) was obtained from the ERA-Interim reanalysis [ 36 ] on a 0.125° horizontal grid. For the global warming projections (see below), the present-day reference temperatures were obtained from the CRU TS version 3.23 dataset, which presents monthly data from the period 1901–2014 on a 0.5° horizontal grid [ 37 ]. To evaluate crop-climate relationships at the different spatial scales, climate data were either spatially averaged for the entire Philippines (here defined by the geographical region 117°E-128°E, 4°N-22°N) or the respective regions (see S1 Table ).

Climate projections

Future climate projection data were obtained from the CMIP5 database [ 38 ] for the business-as-usual scenario RCP 8.5. Monthly output was obtained from eighteen climate models and interpolated using bilinear interpolation to a 0.5° resolution common grid. For the 2°C and 4°C warming targets, we first constructed the canonical global warming temperature pattern [ 39 ] for each of the eighteen models by taking the difference in monthly climatology between the 2080–2099 and 1980–1999 time periods, normalized by the global, annual mean temperature change. The future climate projections are then calculated by adding the change in each (2°C or 4°C warmer) model climatology to the observed (1911–2010) climate history, thus preserving the present-day interannual temperature variability [ 40 ].

Correlation analysis

We utilize standard correlation analysis to investigate the relationships between the respective climate variables and rice production and yield. For these relationships, we consider seasonal anomalies to be independent from anomalies in the same season of the previous and following years, which leads to an effective sample size of 30 (number of years). For all spatial maps that show temporal correlation coefficients in shading for the different geographical regions, an absolute value of the correlation coefficient of ~0.31 is statistically significant at the 90% confidence level using a two-tailed t-test (df = 28). Thus, we are not showing any correlations below an absolute value of 0.3 (white shading) in these maps.

National-level data

Irrigated rice production in the Philippines has almost tripled over the past thirty years, while rainfed rice production has seen a much smaller growth ( Fig 1A ). Over this period, yields for both production systems have increased steadily ( S1A Fig ). Besides this long-term trend, annual rice yields at the national level have been fairly stable over this period, with irrigated paddy rice production having only six yield anomalies exceeding one standard deviation (absolute anomaly of 0.09 [t ha -1 ], which corresponds to ~2.5% of the annual long-term mean in irrigated), while rainfed upland rice crops exhibited eight yield anomalies exceeding one standard deviation (absolute value of 0.07 [t ha -1 ], which corresponds to ~2.9% of the annual long-term mean in rainfed; S1B Fig ). Relative anomalies in total rice production ( Fig 1B ) are larger than those in yield, implying that the effects of climate variability are compounded through both yield and harvested area changes. As a result of the frequent occurrence of natural disasters in the Philippines, production losses are often manageable and built into farm management [ 41 ]. Notable exceptions are 1998 –with two typhoons–and 2010 –with four typhoons, an earthquake and a flood–which both saw large negative production anomalies [ 42 ].

A) Annual rice production in the Philippines; B) Annual rice production anomalies (with regard to a 7 yr moving average); C) Quarterly rice production; D) Normalized quarterly rice production anomalies (the annual cycle is removed and the anomalies are with regard to a 7 yr moving average); E) Normalized quarterly rice yield anomalies. Additionally, d) and e) show the quarterly normalized soil moisture anomalies averaged from 117°E-128°E and 4°N-22°N (black line) and the normalized Niño3.4 index (yellow line). In all panels, R indicates instantaneous correlation except for the correlation coefficients in D) and E) between rice production/yield and soil moisture, which are given for a 3 months lead time of soil moisture, between Niño3.4 and soil moisture for a 4 months Niño3.4 lead time, and between Niño3.4 and rice production/yield for a 7 months Niño3.4 lead time.

Aggregating the yield and production data on an annual time scale potentially masks seasonal modulations of both the large-scale climate variability [ 24 ] and crop-climate relationships [ 25 ]. As a result, quarterly production and yield anomalies [ Fig 1D and 1E ] show more variability than the annual data. Rainfed and irrigated rice production anomalies are substantially less correlated with production in the quarterly data (R = 0.65, significant at the 99% confidence level with df = 28) than in the annual time series (R = 0.86, significant at the 99% confidence level with df = 28). About 10% of variance in anomalous rice production on the national level is related to soil moisture variability, which is strongly negatively correlated with the Niño3.4 index ( Fig 1D ). This reduction of rice production during El Niño events is qualitatively similar to the results of global analyses [ 26 ]. While the correlation coefficients between soil moisture anomalies and rice production anomalies are approximately the same for irrigated (R = 0.33, significant at the 90% confidence level with df = 28) and rainfed (R = 0.34, significant at the 90% confidence level with df = 28) rice production, when looking at yield anomalies the correlation is higher for rainfed than for irrigated systems ( Fig 1E ). This shows that, as expected, irrigation can counter much of the plant physiological response to soil moisture changes (as measured by rice yield), but decisions on planting area (as included in rice production) remain sensitive to water availability [ 25 ].

ENSO impacts on soil moisture

On a regional scale as well as on the national level, the correlation between the Niño3.4 index and soil moisture anomalies in the Philippines is negative ( Fig 2 ), i.e., El Niño events lead to dry conditions in all parts of the country. Interestingly, the correlation between ENSO and soil moisture decreases in the third and fourth quarters ( Fig 2 ). One factor might be that in the summer season rainfall variability is dominated by tropical cyclone activity [ 43 ]. While tropical cyclone activity can be modulated by large-scale climate variability such as ENSO, it can be considered a mostly stochastic process on climate timescales. This wet season (Quarters 3 and 4) is also the season when most rice is planted ( Fig 1C ), indicating that wet-season rice production may be largely decoupled from ENSO variability [ 25 ].

Regional crop-climate relationships

Rice in the Philippines is in the field for 90–110 days, so that planting decisions are made about three months before harvest [ 25 ]. Looking at the lagged correlation between rice production and soil moisture (soil moisture leading by one quarter, Fig 3 ), in most seasons, soil moisture anomalies in the previous quarter are significantly correlated with production variability, with higher soil moisture usually associated with increased rice production. Locally, seasonal correlations can be much higher than the national-level data ( Fig 1D ). A notable exception to this is Quarter 4, when correlations between these two variables are small, or even negative ( Fig 3 ). Production in this quarter is the highest of the year ( Fig 1C ) and represents the wet-season crop. Mean soil moisture conditions during the preceding quarters are high, so that variability in soil moisture does not affect rice planting or yield that much, while the typhoons that often impact the summer season (Q2-Q3) can lead to detrimental flooding in these quarters [ 43 ]. This is in accordance with an analysis of Luzon Island in the Northern Philippines (eight of eighteen regions; [ 25 ]), an area where both mean production ( S2 Fig ) and mean yields ( S3 Fig ) are high.

The annual cycle is removed and production anomalies are with regard to a 7 yr moving average. The soil moisture data are area averaged for each political region corresponding to the rice production data.

Total rice production in any given region is a function of the crop area harvested, the crop yield per unit area, and the number of crops harvested per year. Climate variability influences all of these variables. In Quarter 3, when correlations between soil moisture and total rice production are strongly positive in most regions ( Fig 3 ), there were few locations with significant correlations between previous-quarter soil moisture and rice yield ( Fig 4 ). This means that in this season, soil moisture anomalies might mostly drive planting decisions (i.e., which areas are brought into production), without strongly affecting plant development. During the dry season (Quarters 1 and 2) on the other hand, there are also significant regional correlations between soil moisture and rice yields, implying that climate variability in this season affects both plants and planting decisions. Mean climatological soil moisture conditions thus strongly affect the rice production response to climatic forcing. In contrast to soil moisture, in most regions temperature variability has a much lower correlation with rice yields. In some regions however, ENSO-induced temperature and precipitation changes have an effect in the same direction: El Niño events usually result in dry and hot conditions in the Philippines, which both are associated with a decrease in yield ( S4 Fig ). As we have seen, ENSO is driving a significant part of soil moisture variability in the Philippines which is correlated with rice production variability. Therefore, the predictive skill for ENSO that is seen in operational seasonal forecast models [ 44 ] up to several seasons ahead translates into important information for agriculture management in the Philippines and the possibility to mitigate some of the ENSO-induced effects on rice yields.

The annual cycle is removed and yield anomalies are with regard to a 7 yr moving average. The soil moisture data are area averaged for each political region corresponding to the rice yield data.

The correlation between rice production anomalies in upland rainfed and lowland irrigated systems is stronger in annual data ( Fig 1B ) than in the quarterly data ( Fig 1D ). On a regional level, there is a differential response to climate forcing between these two different management systems. For rice yield in particular, the response to soil moisture changes is, not unexpectedly, stronger for rainfed than for irrigated crops ( Fig 4 ). Previous work found that on a global scale as well, yield losses during El Niño events are greater in rainfed areas compared to irrigated regions [ 26 ]. This shows that irrigation can provide a potentially useful management tool to mitigate climate impacts on rice production in the Philippines. At the same time soil moisture conditions are a direct proxy for local water availability–a major limiting factor for crop yield and production [ 45 ]–which could explain the correlations seen between irrigated rice yields and soil moisture anomalies in Quarter 2 ( Fig 4F ).

When looking at specific regions of high rice production ( S2 Fig ) on Luzon Island (large island in the northern Philippines that includes the regions Cagayan and Central Luzon) and Mimaropa (Southwestern islands within the Philippines), production and yield responses to soil moisture anomalies are not always consistent between these areas (Figs ​ (Figs3 3 & 4 ). Mimaropa exhibits one of the most consistently positive correlations between soil moisture anomalies and crop output in the Philippines, both in terms of total production and crop yield, and in rainfed and irrigated systems alike. In Central Luzon on the other hand, the response is more variable, and correlations are generally low for rice yields. Negative correlations between soil moisture and yield or production in some quarters and regions may reflect the damaging impact of flooding on rice, which happens fairly frequently [ 42 ]. Due to this nonlinear impact of rainfall on rice yield (i.e., an increase of rainfall can lead to either positive or negative rice yield depending on thresholds in the system), the actual yield variance explained by climate might be larger than suggested by linear correlation analysis, which should be explored further in future studies.

Sensitivity to climate in the future

As we have shown here, climate-induced rice production variability in the Philippines over the past three decades has mostly been related to soil moisture changes, which in turn were associated with large-scale inter-annual rainfall variability caused by the El Niño–Southern Oscillation. This is in line with previous studies that show that although rice is grown over a large environmental range in both temperate and tropical areas [ 46 ], more variance in yield in tropical areas is usually due to precipitation (and thus also soil moisture) rather than temperature. Generally, tropical environments have relatively small variability in temperature, so other factors such as solar radiation, precipitation, or soil nutrient availability have a larger impact on crop production [ 47 ]. However, this particular expression of crop sensitivity to large-scale climate may fundamentally change in a warming climate [ 48 ].

In the Philippines, temperatures year-round are currently within the range of favorable growing conditions for rice ( Fig 5 ). Despite the fact that we see a significant proportion of variance explained by ENSO-mediated soil moisture variability, in the future the effect of temperature is likely to become increasingly important: If greenhouse gas emissions continue unabated, by the end of the century summers in the Philippines will be warmer than during the historical record [ 12 ]. Fig 5 shows the year-to-year variability in present-day quarterly temperatures, and how this is projected to change with 2 and 4°C of global warming. Over the past century, quarterly temperatures averaged over the Philippines never exceeded 27°C. With 2°C of global warming, median quarterly temperatures would be outside of the present-day range. With 4°C of global warming, year-to-year temperature variability will be entirely above the range of present-day variability. The effects of this will be particularly impactful during the dry season in Quarter 2, when temperatures are already high and there is low capacity for mitigation through soil moisture.

The variance in future temperatures represents inter-model spread and present-day interannual variability. Occurrence points of rice in the tropics (23.5°S–23.5°N) using quarterly data are plotted in grey, with frequencies rescaled by a factor of 4. Rice location data were downloaded from Genesys PGR [ 78 ].

Under business-as-usual emissions (RCP8.5), the global mean temperature is projected to increase by 2°C as early as 2042, with a median prediction of 2055, and by 4°C between 2075 and 2132. Even in an emissions scenario aiming to stabilize greenhouse gas concentrations by mid-21st century (RCP 4.5), global mean temperature could rise by 2°C as early as 2052 [ 40 ]. Based on the temperature projections for these global warming targets, the Philippines is thus likely to see a fundamental shift in the climate–rice relationship over the course of the next few decades. This analysis focuses only on seasonal-mean temperature projections. However, the average precipitation, inter-annual climate variability, and the frequency of extremes may change as well, but projections for these are much more uncertain.

Regional and quarterly data of climate variability and rice production in the Philippines show that ENSO-induced changes in soil moisture are a major source of climate-driven production variability, especially during the dry season. Wet-season soil moisture changes seem to be more stochastically driven, and therefore more independent from large-scale climate forcing such as ENSO. During this main growing season background soil moisture conditions are high, so factors other than climate drive planting decisions and crop yields. The sensitivity to climate variability is higher in upland rainfed systems than in lowland irrigated systems, and varies strongly by region.

Regional differences in crop-climate relationships could be partly explained by differences in soil type, which determine water-holding capacity and thus soil moisture content and cropping patterns. Other factors that contribute to regional differences include different rice variety choices, different management practices (fertilization, mechanization, planting date, post-harvest storage), as well as different market demands. Cropping calendars also differ across political regions, which creates a differential ability to respond to climate events (e.g., ENSO), accentuating seasonal differences and changing vulnerability. Predictions of ENSO conditions are skillful in the current generation of seasonal forecast models [ 44 ], which translates into information that can be utilized for agriculture management in the Philippines and provides a possibility to mitigate some of the effects of ENSO on rice yields and production. Importantly, extreme ENSO events (such as the 1997/98 El Niño) that lead to large disruptions of the tropical hydroclimate, are projected to occur more frequently by the end of the century in response to greenhouse gas forcing [ 49 ]. Thus, the dual calamity of projected changes of both the climate mean state and ENSO-induced hydroclimate variability will likely constitute significant challenges to future rice production in the Philippines.

Implications for food security in the Philippines

In any given year national production may be adequate, but there might be severe regional shortfalls that impact both food price and security. In the Philippines, regional shortfalls are evident in years when severe natural disasters occurred [ 42 ]. Regions of high mean production ( S2 Fig ) and yield ( S3 Fig ) naturally dominate the signal seen in national production and yield data ( Fig 1 & S1 Fig ). However, individual regions and provinces may experience food insecurity that differ from those seen at the national level and can potentially be more severe. The regional relationships between climate variability and production/yield in combination with both the regional long-term mean production/yield and seasonal climate forecasts might help to mitigate future impacts.

Food in the Philippines is relatively mobile, but food prices are more volatile in years with natural disasters [ 50 ] and yield shortfalls may disproportionately impact small holders [ 51 ]. These negative effects might be mitigated by changing land use patterns, production techniques, or germplasm (breeding material, crop types stored in gene banks, heirloom types, or wild relatives). In the past, land policies in the Philippines have favored expansion of production [ 52 ], focusing on increased planting of annual staples [ 53 ]. This has led to a steady increase in area under cultivation, including areas that were historically used for other crops. Further, domestic Philippine rice production has been incentivized [ 53 ]. As a result, rice yields increased ~1% a year during the second half of the 20 th century due to both management and genetics [ 54 ], while the area of rice production increased by 50% [ 16 ].

Nonetheless, the Philippines are a large importer of rice (~10% of marketed rice per year). This is due geography [ 55 ], international policy pressure [ 56 ], and colonial history [ 56 ], with imports increasing during times of stress (e.g., during the 1997/98 El Niño when rice imports tripled due to fewer harvestable hectares [ 16 ]). This has led to calls for self-sufficiency in rice production which, while possible, would be difficult to achieve with current agricultural policy in the Philippines [ 56 ] that can leave rice markets susceptible to price increases [ 20 ]. It is hypothesized that if there is renewed investment in agriculture, coupled with improved technology and skillful seasonal forecasting, imports could be reduced, helping to increase domestic food security. However, it is unclear if increased investments will provide the necessary buffer to the system to maintain production increases, especially in a changing climate. Additionally, there have been substantial efforts to breed drought resistant rice, with mixed results, due to the trait complexity [ 57 ], though new varieties show promise [ 58 ].

The north-central area of the Philippines is one of the longest continuously-cultivated areas of rice production in the world. Over time, the objectives of breeding and agronomic endeavors have changed, from local heirloom grown on terraces to mega-varieties grown in an industrial setting across millions of hectares [ 59 ]. At the moment, there is increasing interest in heirloom varieties with specific growth environments as a source of both food and export potential [ 60 ]. In subsistence settings, rice farming is supplemented by local trade economies that can increase local food security [ 61 ]. Moreover, there is a complex agricultural landscape established in the northern Philippines, specifically in Ifugao (rice terraces), where historic intensification has been accompanied by extensification [ 62 ]. These examples support the idea that the agro-cultural context can help mitigate the impacts of environmental pressure on food security.

The role of temperature variability

Our results indicate that temperature variability at present is not a big driver of rice production variability ( S4 Fig ). Under continued greenhouse gas emissions however, the range of temperature variability in the Philippines is projected to be outside the present-day envelope by the end of the century ( Fig 5 ). Increasing temperatures will have major implications for rice production in the Philippines. Recent work estimated that for every degree Celsius global temperature increase, global mean rice yields will decline by 3.2 ± 3.7% [ 63 ]. These reductions were projected without consideration of potential CO 2 fertilization, adaptation in agronomic practices, or genetic adaptation [ 63 ]. While a recent meta-analysis identified an increase in yields under increased CO 2 , this may not be an even increase across crops or regions [ 64 ]. Additionally, a comparison between historic and modern cultivars suggests that during modern breeding there has not been a selection for increased response to increased CO 2 concentrations [ 65 ], limiting the potential future CO 2 -fertilization effect.

The temperature sensitivity of crops is dependent on growth stage [ 66 ], time of day, and time of year, but generally a temperature increase of one degree can decrease yields by up to 10% once a temperature threshold is reached in rice [ 67 ; 68 ; 69 ; 70 ]. Due to this nonlinear threshold behavior, the relative importance of temperature variability to yield variability ( S4 Fig ) may increase in a warmer climate. The combined effects of high temperatures and moisture deficits could critically alter the seasonality and locality of the impact of ENSO on rice production. Furthermore, by the end of the century, inter-annual climate variability will regularly push climate in the Philippines outside the climatic range of current tropical gene accessions ( Fig 5 ). Most tropical rice accessions currently grow at quarterly temperatures below 28°C. In a 4°C warmer world, median quarterly temperatures will exceed this threshold year-round. In the second quarter in particular, temperatures will already regularly exceed 28°C with just 2°C of global warming. The performance of tropical rice crops in these climatic conditions has not been tested and is thus potentially a large threat to future food security.

Implications for plant breeding

The ability to increase yields under rising temperatures is a major target for plant breeders [ 71 ]. However, modern crop plants have undergone two significant population bottlenecks–the first during domestication and the second during improvement processes–that have resulted in a significant decrease of the crop’s genetic diversity relative to their wild progenitors [ 72 ]. For instance, modern Asian rice retains ~80% of the genetic diversity of its wild progenitor [ 73 ]. Generally, plant breeding involves crossing 'good by good', a strategy that results in a continuing loss of genetic diversity. Breeding targets focused on yield and quality have often left behind traits from landraces (heirloom lines that have not undergone modern breeding) and crop wild relatives [ 74 ]. Among these are many traits associated with tolerance to abiotic stress associated with climate change [ 75 ]. There have been increasing efforts to collect data surrounding landrace and wild material in germplasm collections (phenotypes, genotypes, biophysical, environmental) [ 74 ], which has led to the creation of a platform to understand the fastest and most practical way to bring in traits from landrace and wild crop material [ 76 ]. Breeding is a long-term endeavor, with a long research and development time [ 77 ]. This lag time requires a forward-looking approach in order to have plant material ready to be used in the field in time for projected changes in climate. By estimating the current and future temperature envelope of rice production in the Philippines, and comparing this to bioclimatic data of collection locations of rice accessions ( Fig 5 ), we have reduced the number of potential parents that could be used to breed for climate change, thus implementing the first stage of utilizing collections for breeding for climate change.

Conclusions

There is an increasing need to understand how climate variability will impact rice yields and production, particularly as human population continues to increase and climate changes. Comparing multiple spatial scales allows for a more complete understanding of what types of policy recommendations should be made, as it allows for a direct partitioning into the political units that are most likely to be effective at driving landscape change. This study identified ENSO as driving a significant part of soil moisture variability in the Philippines, which in turn is correlated with rice production and yield variability. Therefore, skillful seasonal predictions can provide useful information for agriculture management to mitigate climate-induced effects on rice production and yield. Future tropical climates is likely to be outside the range of optimal temperatures for rice production. This is true in the Philippines, and will likely require a modification of both genetics and agronomic practices. Detailed case studies like this will complement global yield impact studies and provide important local perspectives.

Supporting information

The linear correlation coefficient R denotes the simultaneous correlation. a) Annual rice yield in the Philippines; b) annual rice yield anomalies (with regard to a 7 yr moving average); c) quarterly rice yield.

Note that grid point values indicate the mean production value of the whole associated province.

The annual cycle is removed and yield anomalies are with regard to a 7 yr moving average. The temperature data are area averaged for each political region corresponding to the rice yield data.

Acknowledgments

The authors thank Axel Timmermann, Stephen Acabado, and two anonymous reviewers for their valuable comments.

Funding Statement

MFS was supported by the Institute for Basic Science (project code IBS- R028-D1) and the NOAA Climate and Global Change Postdoctoral Fellowship Program, administered by UCAR's Cooperative Programs for the Advancement of Earth System Sciences (CPAESS) and MT was funded by a grant from the Tamaki Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

• 1. Introduction
• a. Typhoon damage area index (DA)
• b. Rice field detection
• c. Typhoon maximum wind speed (Wij)
• d. Regression model
• e. Risk analysis
• a. Typhoon tracks
• b. Provincial rice data
• a. Rice field detection
• b. DA index
• c. Regression results
• d. Quantitative significance
• 5. Conclusions

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Typhoon tracks during 2001–13.

Rice production by province for 2013.

Rice yields by province for 2013.

Rice fields growth onset by quarter for 2013. Data are from rice field detection methods (see section 2 ), and hatching indicates no rice fields.

Area harvested (hectares) vs rice field area detected (pixels).

Distribution of damage to rice (production loss) due to Haiyan. Data are derived from regression estimates (see section 3 ).

Percent reduction in potential quarterly production. Data are derived from regression estimates (see section 3 ).

Estimated return period of damages. Data are derived from risk analysis estimates (see section 3 ).

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Assessing the Impact of Typhoons on Rice Production in the Philippines

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This study quantifies the impact of typhoons on rice production in the Philippines. To this end, satellite-derived reflectance data are used to detect the location of rice fields at 500-m resolution. Utilizing typhoon-track data within a wind field model and satellite-derived precipitation measures, fragility curves are then employed to proxy the damage of storms on rice production within each rice field. The results from a panel spatial regression model show that typhoons substantially reduced local provincial production in the quarter of the strike, having caused losses of up to 12.5 million tons since 2001. Using extreme value theory to predict future losses, the results suggest that a typhoon like the recent Haiyan, which is estimated to have caused losses of around 260 000 tons, has a return period of 13 years. This methodology can provide a relatively timely tool for rice damage assessments after tropical cyclones in the region.

Tropical storms cause considerable amount of damage globally, estimated to be about $26 billion per year ( Mendelsohn et al. 2012 ). In this regard, the Philippines is one of the most cyclone-prone countries in the world. With ~6–9 landfalling storms per year since 1970, it currently ranks second only to China ( Hurricane Research Division 2015 ). One sector of the Philippine economy that is particularly susceptible to these extreme events is rice cultivation. More specifically, typhoons can cause considerable damage to rice production by exposing it to strong winds and excessive rainfall. As a matter of fact, in a study of climate-induced damage to the rice industry since 2007, Israel (2012) estimated that typhoon damage constituted at least 70% of the $276 million (U.S. dollars) of annual damage caused by extreme weather events, including floods and droughts. For a nation like the Philippines, for which rice is the staple food for nearly 90% of the population—providing half their calories and constituting 20% of food expenditures—but which consumes more rice than it produces and where rice accounts for nearly 25% of national agricultural value added, these storms can thus be of particular significance.

The Philippines government has, of course, been aware of the vulnerability of its rice industry to typhoons for a long time and has tried to address the issue through explicit policymaking. More specifically, the National Food Authority 1 (NFA)—the agency in charge of ensuring the stability of the supply and price of rice—imports rice to counteract production shortfalls predicted using seasonal climate forecasts and agricultural production surveys. These import decisions are typically adjusted on a quarterly basis, procurement occurring twice a year, so that imports after a final decision made in January arrive between February and April, just before the rainy season. However, import orders are often readjusted when major events, like a typhoon, cause unexpected production shortfalls. For example, after Typhoon Haiyan in November 2013, the NFA approved the import of a further 355 000 tons of rice in addition to the 350 000 originally procured ( Dela Cruz and Thukral 2013 ). An important difficulty for the NFA in adjusting imports to address shortages due to tropical cyclones is that orders must be made fairly quickly since filling them takes time. However, immediate initial estimates of the actual damage to rice production due to these storms tend to be imprecise, and more accurate assessments have to rely on time-consuming local surveys. In this paper, we provide an approach that will allow more accurate and immediate estimate of the impact of typhoons on rice in the Philippines, which can help policymakers be more effective in their response to these storms.

There is already a small but growing academic literature that has attempted to statistically quantify the impact of tropical cyclones on the agricultural sector. For instance, Chen and McCarl (2009) examine the case of the United States using county-level data of crop production and hurricane intensity measured using the Saffir–Simpson intensity categorization and find different effects across crop types. Spencer and Polachek (2015) , in contrast, employ a hurricane incidence measure for Jamaica parishes and similarly find different impacts for different crops. Examining the Philippines, Israel and Briones (2012) alternatively use the number of typhoons and the incidence of a typhoon of different intensity levels but only find very weak effects on rice production at the province level. Similarly for the Philippines, Koide et al. (2013) note a significant negative correlation between accumulated cyclone energy and provincial rice production. Strobl (2012) finds a negative effect of hurricanes on agriculture in the Caribbean.

In contrast to the previous literature, our study melds multiple methodological approaches to obtain a more accurate estimate of the impact of cyclones on rice production that will reduce measurement error. First, we construct provincial-level estimates of rice damage from localized rice fragility curves, which encompass damage due to both wind and rainfall, rather than using storm incidence or intensity measures. To this end, we take into consideration the location-specific nature of tropical storm destruction, in terms of both wind and precipitation exposure and the rice fields they are likely to affect. 2 More specifically, we first detect the location and growing period of rice paddies at the 500-m level across the Philippines by using satellite-derived information on spectral reflectance and the detection algorithm developed by Xiao et al. (2002a) . This approach allows for the spatial and temporal variations in rice paddies at a spatially detailed level. With the location of rice fields at hand, we then measure local wind exposure using a wind field model and local precipitation exposure during the storm. Damage is then proxied using the fragility curves estimated by Masutomi et al. (2012) . Aggregating these for each quarter at the provincial level and combining them with provincial-level rice data allows us to then statistically estimate the impact on rice production. Also in contrast to the previous literature, we do so within a spatial panel regression framework, which takes account of both potential spatial correlation and spatial spillovers across regions. Finally, we use extreme value theory to predict future losses.

The remainder of the paper is organized as follows. In the next section we describe the number of methodologies employed in our analysis. Section 3 outlines our datasets. Section 4 provides the details and discussion of results. Some final remarks are provided in the last section.

The intensity of wind and rain experienced during a typhoon is fairly heterogeneous even within a relatively small area. Moreover, rice planting can change considerably over space and time. It is thus important to detect rice fields and measure subsequent potential damage due to a storm at the most spatially disaggregated scale as possible. Unfortunately, there is no consistent time series of rice field location for the Philippines at a very spatially disaggregated level available from statistical sources. 4 However, rice paddies possess unique physical features that allow one to use satellite-derived images to proxy field locations. More specifically, rice is first transplanted on a field covered by between 2 and 15 cm of water. The paddy surface is subsequently composed of a combination of water and green growth until about between 50 and 60 days after transplanting when the canopy is totally covered by rice plants. Finally, the leaf moisture and density decreases during the ripening phase until harvest ( Le Toan et al. 1997 ). Importantly, these surface changes allow one to use satellite-derived spectral reflectance data to detect the presence of rice fields based on the temporal combination of the extent of surface water and green vegetation.

Using the LSWI, NDVI, and EVI vegetation indices, we follow the algorithm employed by ( Xiao et al. 2002b , 2005 , 2006 ), which focuses on detecting the flooding/transplanting period and the first part of the crop growth period leading to full canopy expansion. Rice paddy flooding and transplanting is identified using a threshold of either LSWI + 0.05 ≥ EVI or LSWI + 0.05 ≥ NDVI. For each flooded pixel, the identification of rice growing is based on the assumption that rice canopy reaches its maximum within 2 months ( Xiao et al. 2002c ). Therefore, a flooded pixel is considered as a “true rice pixel” if EVI reached half of the maximum EVI value of the current crop cycle within 40 days following the flooding/transplanting date.

Pixels having a high blue band reflectance (≥0.2) but not identified as clouds, which could lead to a false identification of rice paddies, are also removed. Permanent water bodies are distinguished from seasonal water bodies, such as paddy rice, by analyzing the temporal profile of NDVI and LSWI of each cell. More precisely, a pixel is assumed to be covered by water if NDVI < 0.10 and NDVI < LSWI, and it is considered to be a persistent water body if it was covered by water in 10 or more 8-day composite periods within the year. Natural evergreen vegetation areas are also omitted from the analysis to avoid confusing moist tropical regions and mangrove forests, which tend to have similar temporal flooding characteristics as paddy rice fields. In contrast to rice paddies, evergreen forests exhibit consistently high NDVI values throughout the year. Therefore, a pixel for which NDVI ≥ 0.7 over at least twenty 8-day composites during the year was considered an evergreen forest. Since the NDVI forest restriction is a cumulative count, we used a gap-filled product that corrects NDVI values in the time series where clouds were present. In terms of evergreen shrublands and woodlands, one should note that these do not typically have exposed soils, contrary to cropland during postharvest land preparation. Pixels with no LSWI < 0.10 throughout the year were thus considered to be natural evergreen vegetation and therefore not included. Cloudy pixels are removed by using the cloud quality pixel from MOD09A1, where pixels affected by clouds were replaced with a temporary fill interpolated from previous and next composites to obtain a complete time series. Finally, to detect the heading date of each field, we used the day of the maximum NDVI, following Wang et al. (2012) .

c. Typhoon maximum wind speed (W ij )

In terms of implementing (9) , one should note that V m is given by the storm-track data described below, V h can be directly calculated by following the storm’s movements between locations, and R and T are calculated relative to the pixel of interest P = i . All other parameters have to be estimated or assumed. For instance, we have no information on the gust wind factor G . However, a number of studies (e.g., Paulsen and Schroeder 2005 ) have measured G to be around 1.5, so we also use this value. For S , we follow Boose et al. (2004) and assume it to be 1. We also do not know the surface friction to directly determine F . However, Vickery et al. (2009) note that in open water the reduction factor is about 0.7 and reduces by 14% on the coast and by 28% 50 km inland. We thus adopt a reduction factor that linearly decreases within this range as we consider points i further inland from the coast. Finally, to determine B , we employ Holland’s (2008) approximation method, whereas we use the parametric model estimated by Xiao et al. (2009) to derive R max .

Given their relatively small size, provincial rice markets and production in the Philippines are unlikely to be independent, thus potentially inducing some spatial correlation in rice production and area harvested. 6 Importantly, neglecting such spatial correlation in the dependent variable in a regression analysis can lead to biased and inconsistent estimates (see LeSage 2008 ). As is common, we employ a Moran’s test as a first indication whether spatial correlation may be a feature of our data.

Another important aspect of our dataset is that it consists of what is commonly known as “panel data,” that is, we have information about provinces over time. Importantly, this allows one to take account of unobserved factors that may be correlated with both the outcome variable—in our case, rice production—and the explanatory variable of interest, that is, the DA index, which could bias our estimated coefficient. More specifically, with panel data, this can be taken into account by either demeaning all variables or by including a set of unit-level (in our case, province-level) indicator variables. One should note that controlling for province-specific time-invariant unobservable effects means that the estimated coefficients are to be interpreted in terms of within provinces across time impacts rather than across provinces.

In some parts of our analysis, we distinguish between irrigated and rainfed rice production. One should emphasize in this regard that the satellite detection technique described in section 2 does not allow one to explicitly distinguish spatially between irrigated and rainfed rice fields. Thus in those regression models where we examine the impact of typhoons on these different agrisystem types, we assume that their distribution across space is similar to that of all rice fields within provinces. Under this assumption we can use the provincial-level destruction index as representative of the impact of typhoons on both rainfed and irrigated rice fields.

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0214.1

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One of the goals of our analysis is to use our estimates to provide an insight as to the probability of typhoon destruction on rice. In considering the probabilities of these losses, one should note that tropical cyclones are events that can take on extreme values, and thus their distribution function is by definition characterized by heavy tails. Tropical storms as extreme events have generally been studied using the peak over thresholds model (see, e.g., Jagger and Elsner 2006 ), and we here follow suit. The traditional approach in this regard, has been to fit a generalized Pareto distribution (GPD) to the data above a chosen threshold. However, as noted by Scarrott and MacDonald (2010) , the weakness of the GPD threshold approach is that it does not take account of the uncertainty associated with the choice of threshold. As a consequence, a number of extreme value mixture models have been proposed, which encapsulate the usual threshold model in combination with a component capturing the nonextreme distribution, also known as the “bulk distribution.” 8 Here we employ the parametric bulk model proposed by Behrens et al. (2004) , which involves fitting a gamma model for the bulk distribution below the threshold and a GPD above it, where the components of the two distributions are spliced together at the threshold, which is treated as a parameter to be estimated.

Our source for typhoon data is the Regional Specialized Meteorological Centre (RSMC) Best Track Data, which has provided 6-hourly data on all tropical cyclones in the west Pacific since 1951. We linearly interpolate these to 3-hourly positions to be in congruence with our rainfall data described below. We also restrict the set of storms to those that came within 500 km of the Philippines and achieved typhoon strength (at least 119 km h −1 ) at some stage within this distance. 9 In all, a total of 116 typhoon-strength storms traversed the 500-km radius of the Philippines during our sample period of 2001–13. These storm tracks are shown in Fig. 1 , where the darker portion of the tracks indicates where the storms reached typhoon strength. We also list the top 10 most powerful storms in terms of maximum sustained wind speed in Table 1 . As can be seen, the maximum wind speed varied from storms like Haiyan and Megi at 230 km h −1 to slightly weaker ones standing at 205 km h −1 such as Songda and Durian. One may want to note that most of these storms struck in the latter half of our sample period, with a mean year of 2009.

Summary statistics for the top 10 most powerful storms.

Rice data are taken from the Philippine Bureau of Agricultural Statistics Database (Philippine Statistics Authority; available online at http://countrystat.psa.gov.ph/ ). Production is available in total and disaggregated by agrisystem (i.e., rainfed or irrigated) on a quarterly basis at the province level. There are also data on area harvested, from which one can then calculate yields. Although these data series are complete, there were occasions where provinces and subregions of provinces were redefined over our sample period. To have a complete and consistent series, we grouped these together where appropriate. We therefore obtained a balanced panel of 78 provinces over our sample period. Summary statistics of our provincial quarterly production and area harvested data are given in Table 2 . As can be seen, the average quarterly rice production is around 50 000 tons, but with considerable variation. We also decompose the quarterly figures by agrisystem. Accordingly, on average, about 75% of production is from irrigated fields. The spatial distribution of rice production and rice yields presented in Figs. 2 and 3 10 show that the rice production is not evenly distributed across provinces. Similarly, there are considerable differences in rice yields across the Philippines.

Summary statistics of provincial quarterly data.

To control for climatic influences, we consider data on rainfall, water balance, temperature, and radiation. Since rainfall estimates from weather stations are not consistently available on a temporal scale or a spatial scale for the Philippines, we instead use the satellite-derived Tropical Rainfall Measuring Mission (TRMM) adjusted merged-infrared precipitation product 3B42, version 7 ( Goddard Earth Sciences Data and Information Services Center 2015 ). These provide 3-hourly precipitation estimates at a 0.25° × 0.25° spatial resolution. To derive the daily water balance for each rice pixel in our dataset, we use daily reference evapotranspiration (ETo) data at 1° × 1° resolution from the Famine Early Warning Systems Network (FEWS NET) global data portal ( USGS 2015 ) and daily rainfall from the TRMM data to calculate the daily water balance during a rice field season WB, which is calculated as the difference between the two. We also extract minimum and maximum surface temperatures in degrees Celsius at 1° × 1° resolution using data from the Berkeley Earth Surface Temperature (BEST) project ( Rohde et al. 2013 ; NCAR 2015 ). Monthly solar radiation measured in watts per meter squared at 1° × 1° resolution is obtained from the Clouds and Earth’s Radiant Energy Systems (CERES) Energy Balanced and Filled (EBAF) project ( Loeb et al. 2009 ; Loeb and NCAR 2014 ).

4. Results and discussion

Given that one needs at least one year of previous observations to study the temporal variation of the indices outlined above, our period of rice field detection was limited to 2001–13. To this end, we found 1 539 881 different 500-m pixels that were occupied by rice paddy fields at some point in time over our sample period. The mean number of seasons was about 7, but with considerable variability. As an example, we depict the rice fields identified in 2013 and the start of their growing season (in terms of quarter) in Fig. 4 . There is considerable spatial variation of rice fields both across and within provinces, although all provinces have a nonnegligible portion of area dedicated to rice planting for at least some part of the year. Examining the distribution of growing season onset, one can note that this also differs widely across as well as within provinces. This further justifies our use of local field detection methods to try to accurately capture potential damage when a typhoon strikes.

Of course, the reliability of our analysis will depend on the success of the outlined method in detecting rice fields. In this regard one should note that the algorithm is mainly designed for the identification of lowland rice rather than upland rice. In the Philippines, over 95% of rice is of the lowland variety, so that the lack of detection of upland rice paddies is unlikely to play a significant role ( Xiao et al. 2006 ). However, Xiao et al. (2005) developed and verified their algorithm using field data from China, so that one may still wonder about its appropriateness for the Philippines. Reassuringly, a comparison between the satellite-detected fields with extensive local field data from 24 provinces in 2770 locations undertaken by the International Rice Research Institute ( IRRI 2015 ) showed a nearly 80% accuracy rate for the Philippines. As an auxiliary check, we aggregated the area of rice fields across provinces and quarters over our sample period and compared these to quarterly provincial data on area harvested (see Fig. 5 ). Accordingly, there is a clear positive correlation between the two data series. As a matter of fact, regressing the area harvested on the satellite-detected rice field area produced an R 2 of 0.74. Considering all this evidence together, we are fairly confident that our satellite field detection procedure does not involve any excessive amount of measurement error.

We used data from sections 3a through 3c to construct our DA index for all typhoons since 2001. In doing so, we found that, out of the 116 typhoons that came within 500 km of the Philippines, 69 produced positive values of DA. According to the summary statistics in Table 2 , the average quarterly value of DA was about 0.06; that is, potentially about 6% of rice fields were impacted by typhoons in every quarter since 2001. If we consider only those quarters where there was a nonzero potential damage, our index suggests that when a typhoon strikes it will affect about 10% of the rice fields on average per quarter. The largest potential destruction in any quarter was 92%: it occurred in the second quarter of 2013 in the province Siquijor and was due to Typhoon Utor.

One can also examine the value of DA for individual provinces and storms. For instance, the province that was on average most affected since 2001 was Kalinga (DA = 0.16), whereas the least impacted province was Tawi-Tawi. In terms of damage per storm, on average, the damaging storms over our sample period affected about 5% of rice fields per quarter. The most destructive cyclone for rice fields was Utor (total DA = 0.36), 11 whereas Typhoon Haiyan was the second most destructive typhoon in terms of rice fields (total DA = 0.26). One reason that Haiyan was less detrimental to rice than Utor despite being the stronger storm—a maximum wind speed of 230 km h −1 for the former versus 195 km h −1 for the latter—might be that Haiyan occurred at a time when most rice crops were already harvested.

As a first step, we conducted a Moran’s test of spatial correlation for both rice production in each time period and found strong evidence of spatial dependence for all but two quarters. This supported our choice of using spatial panel methods to conduct our regression analysis. Our main regression results are given in Table 3 . As can be seen, the spatial term is significant in all specifications, 12 thus confirming spatial dependence for provincial rice production. In terms of the actual estimated coefficients on the explanatory variables, however, one should note that when ϕ ≠ 0, the estimated parameters cannot be interpreted as marginal effects as in conventional linear models. Instead, the direct effect of a shock in an explanatory variable in a regional unit will not only affect that region’s outcome variable directly, but may also have an indirect impact through feedback effects from its impact on other regions. The magnitude of this spillover effect will depend upon the position of the region in space, the degree of connectivity among region (as determined by the spatial weight matrix), the spatial parameter ρ measuring the strength of spatial dependence, and the magnitude of the estimated coefficient estimates θ and β . Given the difficulty in interpreting the estimated coefficients, we instead follow LeSage and Pace (2014) and calculate the marginal direct and indirect effects of our explanatory variables for all estimations undertaken.

Main regression results. The symbols ** and * depict 1% and 5% significance levels, respectively; standard errors are in parentheses. Dep. = dependent; Prod. = production.

In terms of specifications, we first estimated (10) with only the water balance variable WB, while also controlling for time-invariant province-level fixed effects as well as year and quarter indicator variables. The results in the first numbered column of Table 3 , with only WB, indicate that there is a significant effect of water balance on rice production. In other words, greater net water availability in a region will increase rice production in the region itself. In contrast, there are no indirect impacts of water availability; that is, the water balance of other regions does not induce a change in a region’s own rice production. In the second column, we next introduced our damage index DA. As can be seen, the coefficient on this variable is negative and strongly significant, suggesting that typhoons, by exposing rice fields to strong winds and excessive rain, reduce the production of paddy rice in the Philippines. As with water balance, however, we find no evidence of spillover effects from other regions. We further experimented with including lagged values of our explanatory climatic variables. One now discovers that the effect of water balance is actually lagged rather than contemporary. This may not be surprising given that water as an input is particularly important during the flooding period of the fields and may thus not show up until a quarter after a field is likely to be harvested. Regarding the damage index, in contrast, it is shown that the effect is only contemporary. Again, considering when rice is most sensitive to typhoons (that is, in the heading stage, which occurs relatively late in the growing period), this result may not be too surprising. One may also want to note that when we include the lagged variables, we now also find that there is an indirect effect of DA on rice production. More specifically, the more damage a typhoon induces in spatially close regions, the higher the production in a specific region will be. Nevertheless, given that this result is only significant at the 5% level and that it is dependent on including a lagged value of DA, this result is to be considered with some caution.

Data at the provincial level also separate rice production into irrigated and rainfed cropping categories. The results of rerunning our specification including up to t −1 lags for irrigated and rainfed rice are shown in columns 4 and 5. For irrigated rice, results are similar to the overall sample: water balance has a lagged effect, while DA continues to have a negative and significant contemporaneous impact. We again find that there is now an indirect, although not completely robust, effect of DA on rice production. Our results also suggest, unsurprisingly, that rainfed rice is much more sensitive to water balance, where we find both a contemporaneous and lagged impact, and the coefficients are relatively large. Similarly, as for irrigated rice, typhoons have a significant negative contemporaneous effect on rainfed rice production. Interestingly, the coefficient on this variable is about 33% larger for rainfed rice. However, in contrast to irrigated rice, there appear to be no indirect effects of DA. This may suggest that irrigation technology is better able to deal with the potential damage due to typhoons. For example, irrigation systems may be able to counteract the excessive flooding during a storm. Nevertheless, it must be kept in mind that our assumption about a similar spatial distribution of rainfed and irrigated rice fields may be introducing some measurement error in how well DA captures damage across these two types and thus driving the differences across agrisystem types.

We also experimented with other dependent variables. More specifically, since production is a function of both area harvested and yields, it would be insightful to see how these subcomponents might be affected by the storms. The results for (logged) area harvested, shown in column 6 of Table 3 , indicate that the impact of typhoons is similar to that of production, namely a negative contemporaneous effect. In contrast, for yields, as depicted in the last column, there is no significant impact of typhoons. Thus, overall our results indicate that the fall in production resulting from a typhoon is due to decline in area harvested rather than in a drop in yield.

Our DA index depends in part on the parameters a , b , c , k , and m estimated by Masutomi et al. (2012) based on Japanese data, which may not be exactly the same for the Philippines. As noted earlier, we do not have access to sufficient damage data to estimate these directly for the Philippines. As an alternative, we investigated how changing these parameters might change our estimated effect. More specifically, we calculated the index first using the 5% confidence band values of the estimated parameters and then the 95% confidence band values, as provided by Masutomi et al. (2012) . The subsequent regression results are shown, respectively, in columns 8 and 9 of Table 3 . As can be seen, for both regressions, the direct effect of DA remains statistically significant. Moreover, there is only a marginal difference in their size when compared with the index we used so far, thus suggesting that our estimates are not too sensitive to the chosen parameters, at least for values within a reasonable range.

Finally, we also tried including alternative climatic explanatory factors. Following Welch et al. (2010) and Zhang et al. (2010) , we considered the effect of minimum and maximum temperatures (TMIN and TMAX), precipitation (RAIN), and radiation (RAD). Additionally, as inspired by Peng et al. (2004) , we accounted for the codependent effect of temperature and insolation by including the interaction term TMIN × RAD. Since there was generally no evidence of indirect effects, we only report the direct effects in Table 4 . Similarly to results in Table 3 , results regarding the other explanatory variables are consistent to our earlier specifications, namely that our damage index significantly reduces production and area, but that there is no effect on rice yields. For the other independent variables, we find that TMIN and RAD and their interaction term have a significant effect on rice yields, whereas TMAX is insignificant. An increase in minimum temperature is beneficial to rice yield when radiation is low but detrimental when it exceeds 393 W m −2 . Precipitation changes, however, have a negative effect on rice yields only. When considering the impact on production and area harvested, the effects of TMIN × RAD are inverse from the impact on yields and the lag variables are significant, suggesting an adaptation by farmer to weather conditions detrimental to rice productivity.

As in Table 3 , but for auxiliary regression results.

Our results can be used to determine the recent quantitative importance of typhoons for rice production in the Philippines. For instance, using the mean quarterly total production and our estimated coefficient from column 2, our estimates suggest that, on average, quarterly provincial losses were about 3090 tons over our sample period. This constitutes about 6% of the average provincial quarterly production of rice. Nationally, these losses sum to a median loss of about 46 000 tons per quarter, or a total of 12.5 million tons since 2001. More generally, it is important to note that the implied figures may not only account for damaged rice fields but could also capture other indirect negative effects, such as damage to infrastructure. Unfortunately, the lack of data on the latter aspect does not allow us to disentangle these other factors.

In terms of storm-specific damage, our estimates suggest that each storm has on average reduced rice production by 16 393 tons. In this regard, Typhoon Utor caused the largest damage, totaling about 448 000 tons, whereas, for example, Haiyan resulted in a reduction in production of about 260 000 tons of rice. As an example of the regional distribution of losses, we depict the percentage of rice production lost due to Haiyan in Fig. 6 . Accordingly, much of the loss was in the southern part of the country.

In considering how the implied losses stand relative to what production could have been over time, it is important to realize that there have been considerable changes in production, area harvested, and yields over our sample period. More specifically, examining the aggregate data shows that production and yields have grown by 42%, 17%, and 22%, respectively, since 2001. Thus some of the losses in the earlier period may have been small in part also because yields were smaller. To take account of these changes in our loss estimations, we thus used the coefficient estimated from the sixth column of Table 3 to calculate the implied loss in harvested area, and then converted this to the current equivalent of production by using the average yields by province over 2009–13. 13 This suggests that average losses might have been much greater if past yields had been as high as they are today. For instance, the average yield quarterly adjusted losses would have been 3325 tons, while the median quarterly and total national adjusted losses over our period would been 49 000 and 1.3 millions of tons, respectively. We depict in Fig. 7 these adjusted losses relative to potential production, which is measured as the average potential production in that quarter over our sample period. As can be seen, the quarterly impact varies considerably over our sample period, with relatively loss heavy quarters in the years 2006, 2008, 2010, and 2013. The largest loss was estimated in the second quarter of 2008, where production was more than 25% below its potential.

One can also compare our results with other climatic shocks. More specifically, our estimated coefficients from the second column in Table 3 suggests that a negative shock to water availability—measured as one standard deviation below the mean—causes a reduction of quarterly rice production of 4818 tons. In contrast, the average damaging storm reduces rice production by 11 039 tons. If we take the effect of the lowest observed water balance value relative to the mean, and compare this with the largest provincial DA over our sample period, then the effects are 8440 and 103 520 tons, respectively.

One can also use our results to provide an indication of the losses expected in the near future. To this end, we need to assume that weather, as it is relevant to the formation of typhoons, remains similar to the last 13 years, so that we can use historical data to predict future damage. In fitting the equation of the parametric bulk model of Behrens et al. (2004) to our implied losses from storms calculated earlier, the threshold was found to be 106 021 tons, while the shape and scale parameter were estimated as 0.289 and 113 142, respectively. We used these fitted parameters to estimate the return periods of typhoons inducing various levels of damage and depict these in Fig. 8 . As can be seen, the return period increases with damage levels, although at a decreasing rate. For instance, one should expect a storm causing damages of about 150 000 tons every 5 years, whereas storms causing 400 000 tons are likely every 50 years. In this regard, Typhoon Haiyan was roughly a 1 in 13 year event. However, the accuracy of the return period prediction decreases considerably as one considers more extreme events. For instance, using bootstrapped errors from 500 samples of our data with replacement suggested that for damages of 57 000 tons—which our estimates suggests to be a 2-yr event—the 95% confidence interval was between 1.3 and 2.3 years. For 260 000 tons of damages, that is, a 10-yr event, the 95% confidence band was between 5 and 60 years.

We examined the impact of typhoons on rice production at the province level in the Philippines. To this end, we used satellite reflectance data to detect the location and growth phases of rice fields. We then employed typhoon-track data within a wind field model and gridded rainfall data within a fragility curve to drive a provincial rice damage index. Our spatial panel regression model results showed that typhoons have had a large significant impact on rice production, where national losses since 2001 are estimated to have been up to 12.5 million tons. Using extreme value theory to derive return periods under similar weather conditions to compare the relative differences between cyclones suggested that a storm like the recent cyclone Haiyan—estimated to have caused around production losses of 260 000 tons—is likely to recur every 10 years.

More generally, the methodology outlined here could serve Philippine policymakers in making rapid assessments of the likely losses soon after a typhoon occurrence, and therefore guiding their decision to import rice production to counteract the production shortages. This technique would only require the use of publicly available satellite-derived information and the use of reasonably simple algorithms as employed here. As a matter of fact, the IRRI in the Philippines has already started using satellite data to detect rice fields and used these data to identify flooded areas after Typhoon Haiyan ( IRRI 2013 ). Related to this, the methodology employed here could potentially be used as the underlying tool for introducing a rice insurance product where payouts are triggered according to a parametric index of typhoon-related damage. Again, the IRRI is in the process of introducing the Remote Sensing-Based Information and Insurance for Crops in Emerging Economies (RIICE) to help reduce the vulnerability of rice smallholder farmers in low-income countries globally. The approach here could be one way to incorporate tropical cyclone events as part of such an insurance product. Moreover, the need for such a damage assessment technique may be arguably increasing because of climate-change-induced altering patterns of tropical cyclones and possibly greater exposure due to economic growth in the future.

There are of course a number of aspects of the analysis that could benefit from further work. First, one would ideally like to estimate fragility curves specific to the Philippines. This would require a more comprehensive database of damage for individual cyclones. Second, it must be noted that we were not able to disentangle the effect of typhoons on rice production from other production-reducing factors, such as infrastructure. To isolate such aspects, one would require spatially detailed time series data for these factors.

Acknowledgments

We gratefully acknowledge the financial support for this work from the U.S. Department of Energy Office of Science under DE-FG02-94ER61937, the U.S. Environmental Protection Agency under XA-83600001-1, and other government, industry, and foundation sponsors of the Joint Program on the Science and Policy of Global Change. For a complete list of sponsors, please visit http://globalchange.mit.edu/sponsors/all .

The NFA is the Philippine agency responsible for ensuring the food security of the Philippines and the stability of the supply and price of rice; it has existed since 1981. In 1985 it was granted exclusive authority to import rice. As of May 1999 some importing by the private sector is possible, although these imports, unlike the public ones, carry a large in-quota tariff and thus generally are only a small percentage of total rice imports.

The only other study to do so is Strobl (2012) , which uses gridded 1-km cropland data from the Global Land Cover 2000 database to determine cropland location for the Caribbean, although it does not distinguish between crop types.

Masutomi et al. (2012) also explore alternative distributions.

Time-varying spatial data on rice field location for the Philippines are only available at the aggregate provincial level, with no indication of how rice fields are dispersed within provinces.

In essence, the leaf area index is a quantitative measure of the greenness of plant canopies.

Allen (2014) shows that there is considerable trade in rice between provinces in the Philippines.

We also tested whether a spatial error model might be preferable to an SDM but found no evidence of this.

Despite their obvious advantages, a major drawback with regard to these newer models nevertheless persists, namely, that their asymptotic properties are still little understood.

Tropical cyclones generally do not exceed a diameter of 1000 km.

Since the spatial distribution of area harvested was not significantly different from production, we do not depict this graphically.

Utor struck the Philippines on 12 August 2013. It is estimated to have affected a total of 398 813 people and resulted in about $24.8 million (U.S. dollars) in damages, primarily to the agricultural sector.

Throughout the text, we refer to significant coefficients as those for which the null hypothesis that the coefficient is zero can be rejected at least at the 5% level.

The assumption behind this approach is that rice farmers would have planted the same number of fields even if they could have benefitted from greater yields.

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Sustainable rice farming systems: farmer attribute and land ecosystem perspectives

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