ABSTRACT
This paper investigated how climate change can affect paddy rice productivity and proposed a mitigation measure that may be undertaken. The analysis first explored the potential impacts of climate variables on rice yields by using panel data covering the period 1980 to 2016 for seven countries members of the West African Economic and Monetary Union (WAEMU). It then went on to calculate the investments needed in Research and Development (R&D) for developing new crop varieties technologies by applying the methodology developed by World Bank (2010). Results indicated the growth of rice yields was positively related to the level of technological progress, the rainfall or quantity of water, and amount of seedlings. The concentration level temperature and the mass of carbon dioxide emitted throughout rice cultivation appeared to affect negatively the growth of rice yields. From the estimation of fixed-effects model, the findings showed the existence of specificities (heterogeneity) among the underlying countries that influenced positively rice production in the study area. After all, the aggregate impact of climate change on paddy rice productivity was found to be negative in WAEMU area. From the application of Word Bank methodology, it was recommended to all the countries, in particular, Benin, Burkina-Faso and Niger to increase constantly their annual (R&D) spending throughout the period 2017 to 2050. In consideration of the negative impact of climate variables, the study proposed the development of new rice varieties that could withstand temperature, and a rigorous management of postharvest cropland enabling the reduction of negative externalities due to carbon dioxide emissions.
Key words: Rice productivity, climate change, mitigation, panel data analysis.
For a long time, agricultural sector has been facing so many restraints that hindered its ability to furnish sufficient and affordable quantities of food, as the result of a growing population, rising demands, and fickle diets. Meanwhile, climate change was generating extra pressure on agricultural activities and its effects were expected to be more striking in the future (Foresight, 2011; Lobell et al., 2011). In addition to changing on agricultural productivity, many other impacts were observed (Bryngelsson et al., 2016; Elofsson et al., 2016), including modification in the timing of seasonal occurrences (e.g., earlier flowering of plants). Nevertheless, climate change may also profit agricultural activities in certain areas or countries. Actually, future effects may vary depending on related policies and social development. Accordingly, policies addressing climate change should be oriented either to reduce the sources of greenhouse gases (GHG) (mitigation) or to adjust natural or human systems (adaptation) in response to expected externalities. Today, there exists a consensus between emitters that policies regarding this matter should focus more on mitigation actions. Obviously, whether the outcome of these agreements is attained or not, adaptation measures should be quickly designed in order to diminish the negative impacts on food availability, particularly, in countries which suffer from food security (OECD, 2015; Weldegebriel and Gustavsson, 2017). In that context, Valin et al. (2013) put forward that in developing countries, yield increase could likely mitigate some agriculture-related emissions growth. They also advised the combination of productivity increases in both crop and livestock cultivation in order to exploit mitigation and food security co-benefits. However, the outcome was sensitive to the technological path and which factor benefits from productivity gains. For Sommer et al. (2013), only adaptive changes in sowing dates, cultivated land, farming techniques and in inputs might lead to further yield increases. Their statement followed the fact that yields responses to climate change would likely be related to agro-ecological zones, cultivable land types, crop varieties, agronomic management and future anticipations. Furthermore, Jayne et al. (2014) emphasized the importance to anticipate more the implications of rapidly changing land due to global climate change, while Kjellstrom et al. (2009) stressed the necessity to prevent likewise the consequences on health and labor productivity.
Otherwise, some countries such as those members of the Economic Community of West African States (ECOWAS) may become dependent on food imports outside their geographical space (Egbendewe et al., 2017). As matter at stake, rice foodstuff has drawn attention of previous researches like Tazhibayeva and Townsend (2012), as one of staple crops and main agricultural commodities with regard to human feeding worldwide. Specifically in a large part of the West African Economic and Monetary Union (WAEMU), rice ploughing requires in general high rainfall or ample irrigation, particularly for the most grown variety named paddy rice. As a matter of fact, rice could be grown practically on any field (including a steep hill or mountain) provided that an effective water management system was established. In case of dissatisfaction to this latter, long term changes in means and standard deviations of the climatic variables in a country may have serious impacts on the productivity of rice in different regions (Iqbal and Siddique, 2014). In this context, Zannou et al. (2018) proposed that the technical efficiency of irrigated rice seed should be taken into account when seeking high economic advantage, consistently with the study of Narain et al. (2011) that highlighted the importance of improved crop technologies. Mishra et al. (2018) found that adopting organic rice farming practice would perhaps lead to lower yield, but that loss might be offset by increases in both ecological benefits such as improvement in livelihood and economic gains like rising in prices for producers. However, rice producers may face a lot of challenges in marketing, but not only due to low productivity, but mostly owing to the high competition they do affront with cheaper imported rice, as well as government policy restrictions (Atera et al., 2018). On the basis of the background above, this study sought mainly to understand the climate and paddy rice production nexus, and further, intended to estimate the costs for potential mitigation actions to climate change in seven countries of the WAEMU over the period 1980 to 2016. In this respect, the present research did raise a fundamental question; “how are changes in climate variables linked to paddy rice productivity?” To address this question, the aim of this study did stand twofold: (i) Analyzing how climate change may affect paddy rice yields; (ii) Providing estimates of the magnitude of the mitigation costs for the countries.
MODELLING AND DATA DESCRIPTION
Theoretical model for crop yields simulation
Generally, crop yields are simulated based on climate variables (temperature, precipitation, and CO
2 concentration levels), soil types, and management or technology types. In the context of this research, the yield function used was drawn from previous research (Gornott and Wechsung,
2016) and could be expressed as:
or in logarithmic terms:
where i and t are respectively the individual or country index and time index; Zit represents the technological progress; Tit is the monthly mean temperature; Pit is the monthly mean precipitation; CO2it is the carbon dioxide concentration in the atmosphere at the time t; Sit gathers characteristics relating to soil or management/technology type; and α, β, γ, δ represent the parameters of the model.
Method of calculating future R&D adaption costs
In order to estimate the annual spending in agricultural R&D due to climate change in the WAEMU countries, the World Bank (2010) methodology was applied (Ignaciuk and Mason-D’Croz, 2014). Following this methodology, the R&D spending by 2050 were projected based on Equation 3:
where RDn is the annual expenditures on agriculture R&D, gi represents the historical growth rate of agricultural R&D per country, gc is the historical growth multiplier in Table 1.
Availability of data and Materials
The dataset supporting the conclusions of this article is included within the article and its additional files. The study was based on annual panel data of 37 observations (1980-2016) obtained from different sources, including the Food and Agriculture Organization of the United Nations (FAO) and the World Bank (WB). Table 2 provided variable definitions and data sources related to the crop yields model.
Unit-root test, descriptive statistics analysis, and climate change impacts analysis
Unit-root test on variables
Table 3 showed that all the underlying variables expressed in logarithm were stationary at the level according to Levin et al. (2002) t values method.
Descriptive statistics analysis: Variability among countries
Table 4 provided the countries specificities concerning the average paddy rice yields, as well as the climatic variability and the amount of seedlings. For the overall, the variability of paddy rice yields appeared to be significant in reference to the values of maximum and minimum, on the one hand, and to the value of standard deviation in relation to the mean value, on the other hand. For each country, the statistics also showed a significant discrepancy between the values of standard deviation and their corresponding mean values. However, this discrepancy varied from one country to another, indicating that the magnitude of paddy rice productivity did differ according to factors specific to each country, such as climatic conditions and technologies used (Kabir, 2015). On the basis of their respective mean values compared to the overall mean value, Niger, Senegal and Mali were ranked the top three countries with high yielding of paddy rice over the period of study. It may be established that the seven countries did have the same characteristic feature regarding the prevailing temperature. On the one hand, according to the specific mean values (between 26.6° and 28.8°) compared to the overall mean value (27.9°). On the other hand, according to only the overall value of standard deviation which was practically negligible? Here, the relationship between paddy rice yields and temperature still remained undetermined. The econometric estimation might further throw more light on this unclear liaison. Concerning the specific mean rainfall levels, Cote-d’Ivoire, Togo, Benin and Burkina-Faso appeared to be the luckiest beneficiaries from the goodness of the nature in terms of rainfall comparatively to the overall value of this variable.
The countries of Niger, Mali, and Senegal, seen above as the top in yielding, have meanwhile recorded the lowest mean values of rainfall. This outcome was apparently close to that of Kabir and Golder (2017) by stressing a negative relationship between paddy rice yields and the levels of rainfall. The statistics also showed that Mali, Burkina-Faso, and Cote-d’Ivoire have been the grand emitters of greenhouse gas (CO2-e) coming from rice cultivation, whereas Togo and Benin appeared to be the small emitters. There was difficulty to establish a clear nexus between paddy rice yields and GHG emissions. For example, Mali has yielded a large amount of rice with a high level of GHG emissions, whereas Cote-d’Ivoire has yielded a small quantity of rice with also a high level of GHG emissions. However, the analysis was grounded on Burkina-Faso and Cote-d’Ivoire; it may be realized that paddy rice yields and GHG emissions were negatively linked (OECD, 2013). In addition, it is shown that Cote-d’Ivoire and Mali have consumed the largest amount of seedlings over the period, in reference to the overall value, whereas Benin and Niger have consumed the smallest quantities. This indicated that the scales of investment made in order to extend the agricultural productivity did differ, to a certain extent, from one country to another (Schoneveld, 2014). A remark was the difficulty to establish a clear nexus between paddy rice yields and the amount of seed consumed, meaning that the variability of paddy rice yields did depend on other unrevealed factors or specificities. Indeed, the case of Mali allows pondering on a positive liaison, whereas that of Cote-d’Ivoire did reveal a negative relation.
Estimation of climate change impacts on paddy rice productivity
At first, the Hausman test showed that Random-Effects model (no heterogeneity between countries) was not appropriate. Moreover, the null hypothesis that all the dummy variables are jointly equal to zero could not be accepted (Wald test). Therefore, the suitability of Least Square Dummy Variables (LSDV) model or fixed effects model was admitted in the study. Based on Equation (2), the impacts of climate change on the growth of paddy rice yields were estimated (Table 5). The extent of the impacts varied obviously across the countries. Although climate change was expected to have a negative impact on yields in the majority of cases, in a few cases however, a boost in yields may be expected. Globally, the regression model performed well, predicting 67% of the specified equation correctly. The results showed that the growth of paddy rice yields was affected significantly by all the underlying variables except for LRAIN. In total, a negative sign tended to prevail over the aggregate sum of coefficients relating to the climate variables, highlighting a negative impact of climate change on paddy rice yields in the WAEMU area. This outcome did match up with the work of Egbendewe et al. (2017). The technological progress (TREND) appeared to foster significantly the growth of rice productivity. Henceforth, adopting new innovations through machinery, biotechnology, chemical technology, and new management became a tremendous beneficial action in increasing agricultural growth (Alston, 2010) or rising farm revenue (Berihun et al., 2014). However, with regard to food security goal, policies and actions are needed so as to make the technologies available and affordable to smallholders (Ignaciuk, 2015; Powlson et al., 2011).
The climate variable relating to temperature would impact negatively the growth of paddy rice yields to a great extent. This indicates that global warming would be a serious hindrance to the growth of rice productivity in the WAEMU area over the years. Consistently, IPCC(
2014), Nelson et al. (2014) and Rosenzweig and Parry (
1994) do alert that large decreases in agricultural productivity are even expected to occur in the developing countries by 2050, depending on the harshness of the climatic shocks. However, their statement was made on the basis of assumption that no action was carried out to define appropriate strategies for adaptation and to reduce the GHG emissions. On the other side, the outcome contradicted the study of Sommer et al. (2013) indicating that the increase in temperature in response to climate change was the most important factor that led to earlier and faster crop growth in Central Asia. The climate variable pertaining to rainfall would have a positive impact on the growth of paddy rice yields in the WAEMU area. In other words, when the quantity of water is sufficient, it fosters paddy rice productivity. The result here was similar to that obtained by Sommer et al. (2013). However, the variable appeared insignificant in this study, perhaps due to the richness in hydrologic endowments that the nature has bestowed upon the majority of the countries. Looking it in a specific way, the variable might be significant for some countries and insignificant for others. The increase in the concentration level of GHG (CO
2-e) emitted would influence negatively the growth of paddy rice yields in the WAEMU area, but this harmful impact appeared less insistent than that provided by the rising of temperature (OECD, 2013).
The amount of seedlings applied to soil was destined to capture the impact of extensive agriculture. It was found that as the number of tons of seedlings increased, so did the growth of paddy rice yields in the WAEMU area. The outcome showed that the positive benefits drawn from a large scale seedling production might offset the negative externalities generated by GHG emissions (Schoneveld, 2014). Moreover, the results indicated that all the country dummies were positively significant, emphasizing the existence of specificities inherent to each country that need to be taken into account while analyzing the impact of climatic conditions on rice productivity (Tazhibayeva and Townsend, 2012). These specificities could be, among others, effective irrigation system, sustainable farming practices (e.g. multi-cropping, crop rotation and agroforestry), or relevant management system (e.g. integrated nutrients management and integrated pests
management).
Adaptation to climate change impacts
Identifying a set of adaptation options
Table 6 provided a picture of measures that may affect adaptation to climate change.
Estimation of R&D costs
Building an agricultural R&D system (e.g. developing new technologies that increase yields) was proved to bring an undeniable boon to countries for mitigating climate change negative impacts (Fuglie and Wang, 2012). Assuming that there was an easy access to high-quality seeds on a market of perfect competition, FAO (2015) recommended that the use of improved varieties of seeds shall continue so as to enhance crop productivity gains in the future. Evidence (Oleson and Porter, 2009) also revealed that rotating species may foster soil erosion control, nutrient loss reduction and genetic diversity. Following the same approach as Wreford et al. (2010), the orders of magnitude for the potential expenditures in R&D that would be needed globally to develop new crop varieties (rice variety included) was calculated as shown in Table 7. Thus ceteris paribus, Benin, Burkina-faso and Niger were recommended to increase constantly their annual R&D spending by approximately 3.2, 1.9 and 0.8% respectively throughout the period (2017-2050). Cote-d’Ivoire, Mali, Senegal and Togo, meanwhile, were advised to maintain or, if desired, increase their R&D planning.
This study aimed mainly to understand the nexus between climate variables and paddy rice productivity in seven countries members of the West African Economic and Monetary Union (WAEMU), and then, proposed a mitigation measure that may be undertaken. The analysis first explored the potential impacts of climate variables on rice yields by using panel data covering the period 1980 to 2016. It then went on to calculate the investments needed in R&D for developing new crop varieties (World Bank, 2010). The results showed that the growth of paddy rice productivity was positively affected by the level of technological progress, the rainfall or quantity of water, and the amount of seedlings. In contrast, the concentration level of temperature (global warming) and the mass of carbon dioxide appeared to be negatively related to the growth of rice yields. Obviously, the extent of the impacts varied according to the specificities (heterogeneity) among the countries, which was admitted through the positive significance of country dummy variables. However, the findings retained that the aggregate impact of climate variables on paddy rice productivity was negative. In this respect, some countries namely, Benin, Burkina-faso and Niger were strongly recommended to increase constantly their annual R&D spending throughout the period 2017 to 2050 so as to face the consequences of the global warming and then, withstand food insecurity. This suggestion was very close to that of Schoneveld (2014) in terms of policies and strategies seeking to minimize damages due to climate change in both developed and developing areas.
The authors have not declared any conflict of interests.
The authors were grateful to all anonymous persons for their qualitative contributions or comments on the paper.
REFERENCES
|
Alston J (2010). The benefits from agricultural research and development, innovation and productivity growth, OECD Food, Agriculture and Fisheries papers 31, OECD, publishing, Paris.
|
|
|
|
Atera E, Onyancha F, Majiwa E (2018). Production and marketing of rice in Kenya: Challenges and opportunities. J. Dev. Agric. Econ. 10(3):64-70.
Crossref
|
|
|
|
Berihun K, Bihon K, Kibrom A (2014). Adoption and impact of agricultural technologies on farm income: evidence from southern tigray, northern ethiopia. Int. J. Food Agric. Econ. 2(4):91-106.
|
|
|
|
Bryngelsson D, Wirsenius S, Hedenus F, Sonesson U (2016). How can the EU climate targets be met? A combined analysis of technological and demand-side changes in food and agriculture. Food Policy 59:152-164.
Crossref
|
|
|
|
Egbendewe A, Lokonon B, Atewemba C, Coulibaly N (2017). Can intra- regional food trade increase food availability in the context of global climatic change in West Africa? Climatic Change 145(1-2):101-116.
Crossref
|
|
|
|
Elofsson K, Bengtsson N, Matsdotter E, Arntyr J (2016). The impact of climate information on milk demand: Evidence from a field experiment. Food Policy 58:14-23.
Crossref
|
|
|
|
Food and Agriculture Organization (FAO) (2015). Climate change and food systems: global assessments and implications for food security and trade. Food and Agriculture Organization (FAO), Rome, 356 pp. Google Scholar.
View
|
|
|
|
Keith F, Sun LW (2012). Productivity Growth in Global Agriculture Shifting to Developing Countries. Choices, 27(4):1-7.
View
|
|
|
|
Foresight (2011). Future of food and farming: executive summary. Government Office for Science, London UK, Ref: BIS/11/547, 44 pp.
|
|
|
|
Gornott C, Wechsung F (2016). Statistical regression models for assessing climate impacts on crop yields: a validation study for winter wheat and silage maize in Germany. Agric. For. Meteorol. 217:89-100.
Crossref
|
|
|
|
Ignaciuk A (2015). Adapting agriculture to climate change: a role for public policies. OECD Food, Agriculture and Fisheries papers, N°85, OECD, publishing, Paris.
Crossref
|
|
|
|
Ignaciuk A, Mason-D'Croz D (2014). Modelling adaptation to climate change in agriculture. OECD, Food, Agriculture and Fisheries papers, N°70, OECD publishing, Paris.
Crossref
|
|
|
|
Intergovernmental Panel on Climate Change (IPCC) (2014). Climate change 2014: impacts, adaptation and vulnerability. Working group II, contribution to the IPCC 5th assessment report.
|
|
|
|
Iqbal K, Siddique M (2014). The impact of climate change on agricultural productivity: evidence from panel data of Bangladesh. Discussion paper N°14.29.
|
|
|
|
Jayne T, Chamberlin J, Headey D (2014). Scarcity amidst abundance? Reassessing the potential for cropland expansion in Africa. Food Policy 48:51-65.
Crossref
|
|
|
|
Kabir H, Golder J (2017). Rainfall variability and its impact on crop agriculture in southwest region of Bangladesh. J. Climatol. Weather Forcasting 5:196.
|
|
|
|
Kabir H (2015). Impacts of climate change on rice yield and variability: an analysis of disaggregate level in the southwestern part of Bangladesh especially Jessore and Sathkhira districts. Geogr. Nat. Disasters 5:148.
Crossref
|
|
|
|
Kjellstrom T, Kovats R, Lloyd S, Holt T, Tol R (2009). The direct impact of climate change on regional labor productivity. Arch. Environ. Occup. Health 64(4):217-227.
Crossref
|
|
|
|
Levin A, Lin C, Chu C (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. J. Econom. 108:1-24.
Crossref
|
|
|
|
Lobell D, Schlenker W, Costa-Roberts J (2011). Climate trends and global crop production since 1980. Sci. 333(6042):616-620.
Crossref
|
|
|
|
Mishra A, Kumar A, Joshi P, D'Souza A, Tripathi G (2018). How can organic rice be a boon to smallholders? Evidence from contract farming in India. Food Policy 75:147-157.
Crossref
|
|
|
|
Narain U, Margulis S, Essam T (2011). Estimating costs of adaptation to climate change. Climate Policy 11(3):1001-1019.
Crossref
|
|
|
|
Nelson G, Valin H, Sands R, Havlík P, Ahammad H, Deryng D, Elliott J, Fujimori S, Hasegawa T, Heyhoe E, Kyle P, Von Lampe M, Lotze-Campen H, Mason d'Croz D, van Meijl H, van der Mensbrugghe D, C Müller, Popp A, Robertson R, Robinson S, Schmid E, Schmitz C, Tabeau A, Willenbockel D (2014). Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 111(9):3274-3279.
Crossref
|
|
|
|
Organisation for Economic Co-operation and Development (OECD) (2015). Aligning policies for a low carbon economy. OECD publishing, Paris.
|
|
|
|
Organisation for Economic Co-operation and Development (OECD) (2013). Greenhouse gas emissions: Climate change, in OCDE. OECD compendium of agri-environmental indicators. OECD, publishing, Paris.
|
|
|
|
Oleson J, Porter J (2009). Deliverable 10: Adaptation and mitigation. PICCMAT projet report.
|
|
|
|
Powlson D, Gregory P, Whalley W, Quinton J, Hopkins D, Whitmore A, Hirsch P, Goulding K (2011). Soil management in relation to sustainable agriculture and ecosystem services. Food Policy 36:72-87.
Crossref
|
|
|
|
Rosenzweig C, Parry M (1994). Potential impact of climate change on world food supply. Nature 367:133-138.
Crossref
|
|
|
|
Schoneveld G (2014). The geographic and sectoral patterns of large-scale farmland investments in sub-Saharan Africa. Food Policy 48:34-50.
Crossref
|
|
|
|
Smith B, Skinner M (2002). Adaptation options in agriculture to climate change: A topology. Mitigation and Adaptation Strategies for Global Change 7:85-114.
Crossref
|
|
|
|
Sommer R, Glazirina M, Yuldashew T, Otarov ??, Ibraeva M (2013). Impact of climate change on wheat productivity in central Asia. Agric. Ecosyst. Environ. 178(2):78-99.
Crossref
|
|
|
|
Tazhibayeva K, Townsend R (2012). The impact of climate change on rice yields: heterogeneity and uncertainty. In the Intitute for International Economic Studies Conference on Climate and the Economy, Harvard. CiteseerX working paper.
|
|
|
|
Valin H, Havl’ık P, Mosnier A, Herrero M, Schmid E, Obersteiner M (2013). Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security? Environ. Res. Lett. 8:035019 (9 pages).
|
|
|
|
Weldegebriel N, Gustavsson H (2017). Climate change adaptation and mitigation strategies vis-à-vis the agriculture and water sectors in Ethiopia. Case review/study of the EPCC (Ethiopian Panel of Climatic Change) project. Environment Pollution and Climate Change 1: P 125.
Crossref
|
|
|
|
World Bank (2010). Economics of adaptation to climate change: synthesis report. World Bank, Washington, DC.
View
|
|
|
|
Wreford A, Moran D, Adger N (2010). Climate change and agriculture: impacts, adaptation and mitigation. OECD, publishing, Paris.
|
|
|
|
Zannou A, Kpenavoun-Chogou S, Saliou I, Biaou G (2018). Technical efficiency of irrigated rice seed farmers in Koussin-Lélé, Benin Republic. J. Dev. Agric. Econ. 10(1):28-37.
Crossref
|
