Can domestic wheat farming meet the climate change-induced challenges of national food security in Uzbekistan?

Babadjanova, Mashkhura; Bobojonov, Ihtiyor; Bekchanov, Maksud; Kuhn, Lena; Glauben, Thomas Article — Published Version Can domestic wheat farming meet the climate changeinduced challenges of national food security in Uzbekistan? International Journal of Water Resources Development Provided in Cooperation with: Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale) Suggested Citation: Babadjanova, Mashkhura; Bobojonov, Ihtiyor; Bekchanov, Maksud; Kuhn, Lena; Glauben, Thomas (2024) : Can domestic wheat farming meet the climate change-induced challenges of national food security in Uzbekistan?, International Journal of Water Resources Development, ISSN 1360-0648, Taylor & Francis, London, Vol. 40, Iss. 3, pp. 448-462, https://doi.org/10.1080/07900627.2023.2290523 , https://www.tandfonline.com/doi/full/10.1080/07900627.2023.2290523 This Version is available at: https://hdl.handle.net/10419/294171 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Can domestic wheat farming meet the climate change-induced challenges of national food security in Uzbekistan? Mashkhura Babadjanova a,b , Ihtiyor Bobojonov b , Maksud Bekchanov c,d,e , Lena Kuhn b and Thomas Glauben b a International Agricultural Economics, Tashkent State Agrarian University, Tashkent, Uzbekistan; b Department of Agricultural Markets, Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale), Germany; c Department of Environment Sciences, Informatics and Statistics (DAIS), Ca’ Foscari University of Venice, Venice, Italy; d Economic Impacts of Climate Change and Policy (ECIP) Division, Euro-Mediterranean Center on Climate Change (CMCC), Venice, Italy; e RFF-CMCC European Institute on Economics and the Environment (EIEE), Milan, Italy ABSTRACT This study is the first to develop food supply and demand projections over the 21st century for Uzbekistan by considering the combined effects of climate change and soil salinization. The study results suggest that rising summer temperatures and soil salinity will considerably reduce wheat production. Projections indicate that a large wheat supply–demand gap will emerge in the midterm, particularly under the SSP3-RCP7.0 scenario. For the two more pessimistic scenarios, supply losses of about 24–29% are expected by the end of the century. Supply–demand gaps of up to 2.7 million tons of wheat would pose serious challenges to national food security. ARTICLE HISTORY Received 9 May 2023 Accepted 28 November 2023 KEYWORDS Climate change impact; food security; irrigated wheat farming; crop yield simulation; soil salinity; Uzbekistan Introduction Global climate change poses a serious threat to the ecosystems, food production and industries in many parts of the world (Intergovernmental Panel on Climate Change [IPCC], 2022). Uzbekistan is one of the hotspots of global warming, with numerous studies having made evident that global warming is causing a reduction in river runoff and increasing water scarcity in the region (Gosling & Arnell, 2016; Reyer et al., 2017). Irrigated agriculture, which accounts for about 90% of total water withdrawals in Central Asia, is the most vulnerable to temperature variations and reduced water supply (Frenken, 2013). Increasing crop water consumption requirements and evaporation losses from irrigation systems have already added to the water deficit challenge in the region (Reyer et al., 2017). Recent research has focused on the impact of climate change on total factor productivity growth in agriculture CONTACT Mashkhura Babadjanova [email protected] Supplemental data for this article can be accessed at https://doi.org/10.1080/07900627.2023.2290523. INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 2024, VOL. 40, NO. 3, 448–462 https://doi.org/10.1080/07900627.2023.2290523 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. (Kirui et al., 2023; Ortiz-Bobea et al., 2021). Climate change-induced water supply reductions in the region will lead to considerable financial losses within and outside agriculture (Bekchanov, 2014; Bekchanov & Lamers, 2016). Previous studies have suggested that climate change might have a positive impact on certain subregions in the short term (Mirzabaev, 2013; Sommer et al., 2013); however, widespread negative effects are inevitable in the midterm (Bobojonov & Aw-Hassan, 2014; Bobojonov et al., 2016). Another serious challenge for Uzbek agriculture is soil salinization, which has resulted in considerable decreases in agricultural production (Khasanov et al., 2023). Soil salinization and land degradation have become the most pressing problems in the Aral Sea region of Central Asia, particularly in Uzbekistan, with 73.8% of the area around the South Aral Sea being moderately saline and 7.5% being slightly saline (Duan et al., 2022). The primary causes of soil salinization in Uzbekistan are poor land and groundwater management (Eswar et al., 2021; Ibrakhimov et al., 2007), and the use of phosphorus-based mineral fertilizers (Khasanov et al., 2023). Soil salinization is intensified due to changes in weather variability and water availability in dry regions, with shallow groundwater levels under water-scarce conditions (Corwin, 2021; Greene et al., 2016). Higher temperatures due to climate change reduce water availability, increase evapotranspiration and consequently require additional irrigation water applications, when saline water from groundwater aquifers or other alternative sources is often used to meet crop water demand (Mukhopadhyay et al., 2021; Okur & Örçen, 2020; Schlenker et al., 2005). If irrigation water applications are reduced due to lowered water supplies, capillary increases due to high evaporation induced by climate change also lead to higher soil salinity around the crop root zones. Despite this vicious circle, the effects of soil salinity have not been fully considered in most of the previous projections on the effects of climate change on crop yields (e.g., Hatfield et al., 2015; Lobell et al., 2011). Our study contributes to ongoing research by using granular data and implementing panel data analysis to model the combined effects of soil salinization and climate change on the wheat supply of Uzbekistan, one of the hotspots of climate change. This approach allows for a detailed spatial analysis of the climate change impacts on wheat yields, a strategic staple crop across Uzbekistan. Differing from other approaches, such as the Ricardian approach, the panel data approach allows for controlling time-invariant omitted variables beyond temperature and precipitation (Deschenes & Greenstone, 2012). Moreover, by regressing crop yields on biophysical factors, we developed a statistical crop yield model based on empirical data, which is more reliable than biophysical crop simulation models (Ortiz-Bobea & Tack, 2018). Additionally, we applied a ‘shared socio-economic pathways–radiative concentration pathways’ (SSP-RCP) climate scenarios framework (van Vuuren et al., 2014) to the context of Uzbekistan and integrated it with the estimated ‘climate-wheat yield’ function to assess the future climate change impacts on food demand and supply for the first time in Uzbekistan. According to our projections, the supply of wheat is anticipated to decrease considerably due to heat and salinity stress, resulting in substantial food demand supply gaps after 2040. Given the limitedness of available croplands and water resources, significant improvements in fertilizer-use efficiency, soil management and the development of highyielding varieties will be necessary to ensure local food security in the future. INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 449 Data and methods Study area Uzbekistan is a double land-locked country located in the centre of Central Asia (Figure 1). Over 40% of its 35.6 million people reside in rural areas (State Statistics Committee of Uzbekistan [SSCU], 2022). Due to consistent population growth, national food security has become a matter of significant political importance in the country. The climate of the country is extremely continental, with an average monthly temperature of 13.7°C, peaking at 28.5°C in July (Figure 2). Temperatures can plunge to −35°C in the north of the country during winter and soar to 50°C in the south during summer (as observed from 1991 to 2020). The country experiences a wide range of annual precipitation, varying between 200 and 550 mm in most areas. Yet, areas in the desert and steppe zones receive less than 200 mm, while mountainous areas receive more than 900 mm (Khalikulov et al., 2016). Agricultural production plays a pivotal role in the national economy, contributing 16% of gross domestic product (GDP) and employing 44% of the labour force (Djanibekov et al., 2010; SSCU, 2022). Wheat production, in particular, is crucial for national food security, as flour-based food products provide over 52% of the dietary caloric intake (Abdullaev et al., 2009; Chabot & Tondel, 2011). Following the collapse of the former Soviet Union’s unified food supply and distribution system in 1991, Uzbekistan’s access to strategic food imports became limited and insecure. Consequently, the government introduced agricultural reforms to increase domestic wheat production and enhance ‘independence in cereals’ through policies such as setting wheat production targets, subsidizing inputs (irrigation water, mineral fertilizers, fuel, machinery), and regulating wheat and flour prices (Lombardozzi & Djanibekov, 2021). Figure 1. Uzbekistan and its provinces. Source: Authors’ own illustration. 450 M. BABADJANOVA ET AL. Thanks to the reforms, the area of national wheat production increased rapidly from about 627,000 to over 1.3 million ha between 1992 and 1996, allowing the country to achieve independence in cereals (Figure 3). The expansion of wheat production was achieved through reducing areas previously used for fodder production and some cotton areas as well. The country’s wheat yields have gradually increased over the years, reaching Figure 2. Average monthly temperature and rainfall in Uzbekistan, 1991–2020. Source: Authors using data from World Bank (2022). Figure 3. Wheat yields and harvested area in Uzbekistan, 1992-2020. Source: Authors based on data from FAO (2023). INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 451 4900 kg/ha in 2009 due to the introduction of suitable wheat varieties based on each region’s soil and climatic conditions. Another driver was farmers’ improved knowledge and experience with cultivation practices and wheat varieties. Currently, wheat and cotton are the most essential crops and are widely cultivated throughout the country. Wheat cultivation areas that are irrigated account for 85%, while the remaining 15% are areas of rainfed farming. However, almost all the rainfed wheat farming areas are located in the provinces of Kashkadarya, Jizzakh and Samarkand. Irrigated wheat farming takes place in late autumn, winter and spring, thus winter and spring precipitation is crucial for the performance of irrigated wheat farming. During the winter and spring, the ample precipitation saturates the soil with moisture, reducing the need for additional irrigation. Analytical approach and data To assess wheat supply and demand projections, we calculated the production elasticities for important production and climate factors in the first step. These calculated production elasticities were then used to develop the supply projections. Production elasticities For our analysis in the first step, we employed a Cobb–Douglas production function in log form: where Yit represents the total output in district i and at time t; b is the slope coefficient; the variable Land stands for total harvested land (thousands ha); Labour is the labour value used; Fert is total (chemical) fertilizer; IW represents the total amount of irrigation water; T represents the average temperature for each season (W, winter; Sp, spring; S, summer; A, autumn); P represents each season’s total precipitation; GWS represents groundwater salinity; SS represents soil salinity; and uit is the error term. A fixed effect model can capture the relationship between agricultural production and climate variables by considering that unobserved time-invariant factors or unobserved variables are constant (Allison, 2009). Many prior studies have used fixed-effect models to measure the impact of climate change on different crops within a panel data framework (Ahmad et al., 2014; Mirzabaev, 2013; Sadozai et al., 2019). Additionally, a Hausman test confirmed the suitability of a fixed effects model for our data. To analyse the climate, biophysical and economic factors influencing wheat productivity, we constructed a panel dataset encompassing the years between 2008 and 2017, and 158 of the country’s 176 districts (SSCU, 2022). We obtained the necessary data from multiple sources, including Beaudoing et al. (2020), Rodell et al. (2004), SSCU (2020), the Ministry of Water Resources of the Republic of Uzbekistan (MWRRU, 2020), and the Global Rainfall Map in Near Real-Time (GSMaP_NRT) (2023) prepared by the JAXA Global Rainfall Watch of Japan’s Aerospace Exploration Agency. Particularly, we collected province-level secondary data on the total wheat yields and harvested area across districts for both irrigated and rainfed farming from the SSCU (2020). 452 M. BABADJANOVA ET AL. Data on the total costs of nitrogen and phosphorus and labour use were also obtained from the same source (SSCU, 2020). Data on district level irrigation water use for September– December and March–May were provided by the regional branches of the MWRRU (2020). We excluded January and February as the soil is typically frozen in these months, and hence no irrigation or leaching takes place. Data on temperature were extracted from global climate databases (Beaudoing et al., 2020; Rodell et al., 2004). We used the mean temperature for the relevant period from September to June, omitting the (post-) harvest period of July–August. Data on precipitation, particularly cumulative rainfall across districts for the same period were derived from the GSMaP_NRT prepared by the JAXA Global Rainfall Watch of Japan’s Aerospace Exploration Agency (see also Eltazarov et al., 2021). Data on monthly soil and groundwater salinity levels were obtained from the regional branches of MWRRU (2020), and we calculated annual salinity levels as the average of monthly levels. Table 1 provides summary statistics and definitions of the variables used in the regression model. The dependent variable in the model is the log of total wheat production. The average wheat production across the districts is 39,217 tons; the average wheat sown area is 8283 ha. Given the data availability, some intermediate inputs in wheat cultivation, such as labour and fertilizers, are measured in economic terms (in UZS), while irrigation water is measured in physical terms (thousands m 3 ). The average fertilizer cost per district is 2.498 million UZS and the average labour cost per district is 2.357 million UZS. For 10 months, the district-level average irrigation water use is 134,000 m 3 . The average temperature across the districts in winter, spring, summer and autumn is 1.5, 16.5, 27.8 and 14.7°C, respectively. Precipitation almost does not occur in June, and irrigated areas receive about 80 mm of rain or snowfall per season in winter and spring on average. Due to seasonal changes, the average salinity levels of soil and groundwater are reported as annual averages, ranging from 0.1 to 7.6 g/l. Lower soil salinity levels of 0.1–0.4 g/l are found in the regions of Samarkand and Namangan, while higher soil salinity levels of 2.1 g/l are detected in regions around the Aral Sea. The highest salinity level of 7.6 g/l is found in the regions of Karakalpakstan, Khorezm and Bukhara. Table 1. Summary statistics. Variables Description Mean SD Minimum Maximum Total production Total wheat production (tons) 39.217 23.901 38 139.840 Land Total wheat area sown (ha) 8.283 5.552 105 52.200 Labour Labour costs (1000 UZS) 2.357 2.195 9 17.469 Fertilizer Fertilizer costs (1000 UZS) 2.498 2.003 13 17.690 Water used for irrigation Total irrigation water, September–May (January– February excluded) (1000 m 3 ) 134 72 0.6 467 Winter temperature Mean temperature for winter (December–February) (°C) 1.5 3.4 −15.6 10.1 Spring temperature Mean temperature for spring (March–May) (°C) 16.5 3.5 −12.3 24.0 Summer temperature Mean temperature for summer (June) (°C) 27.8 3.7 4.0 34.5 Autumn temperature Mean temperature for autumn (September– November) (°C) 14.7 2.7 −2.8 21.4 Winter precipitation Total precipitation for winter (December–February) (mm) 82.2 45.0 2.0 270.6 Spring precipitation Total precipitation for spring (March–May) (mm) 82.9 49.8 1.6 273.7 Summer precipitation Total precipitation for summer (June) (mm) 7.9 8.6 0 69.9 Autumn precipitation Total precipitation for autumn (September– November) (mm) 44.5 37.8 1.2 243.9 Groundwater salinity Average annual salinity (gr l −1 ) 2.1 1.5 0.2 11.8 Soil salinity Average annual salinity (gr l −1 ) 2.1 1.3 0.1 7.6 Note: Exchange rate: US$1 = UZS 10,920. INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 453 Wheat supply and demand projections To estimate future wheat supply, we integrated the production model described above with existing climate projections’ data from the Global Climate Knowledge Portal (World Bank, 2022), which provides seasonal temperature and precipitation projections across Uzbekistan from 2020 to 2100 under various SSP-RCP scenarios. To develop wheat demand projections, we considered national population changes over time under SSP scenarios (IIASA, 2023) and average dietary wheat consumption requirements per capita. As the Uzbek wheat production area has not changed over the last decade (Figure 3), we assumed it to remain constant in the future. Representative concentration pathways (RCPs) are trajectories of atmospheric greenhouse gas concentrations as adopted by the IPCC (2022). These pathways correspond to possible warming levels over the years triggered by greenhouse gas emissions (Van Vuuren et al., 2011). The RCPs considered here, including RCP2.6, RCP4.5, RCP7.0 and RCP8.5, are labelled based on a possible range of radiative forcing in 2100. Higher values of RCP stand for higher levels of greenhouse gas (GHG) emissions and thus higher levels of temperature increases by 2100, while lower levels of RCP are related to lower temperatures but require high climate change mitigation efforts. Since the IPCC’s Fifth Assessment Report (2022), RCPs have been considered together with so-called shared socio-economic pathways (SSPs). The SSPs represent socioeconomic developments up to 2100 and are used to elaborate GHG emission scenarios under various climate policies. Five SSP scenarios are: SSP1 – Sustainability (‘Taking the Green Road’), SSP2 – ‘Middle of the Road’, SSP3 – Regional Rivalry (‘A Rocky Road’), SSP4 – Inequality (‘A Road Divided’), and SSP5 – Fossil-fuelled Development (‘Taking the Highway’) (Van Vuuren et al., 2014). In brief, SSP1 stands for low levels of barriers to climate change adaptation and mitigation, and corresponds to low levels of global temperature rises, being the most desirable among all SSPs; SSP2 and SSP3 considering moderate and high levels of barriers for climate change adaptation and mitigation, respectively. SSP4 represents low levels of difficulty for introducing mitigation options but strong barriers to adaptation efforts. SSP5 considers high levels of difficulty to implement mitigation measures but low levels of challenges for adopting adaptation options. Four combinations of climatic (RCP) and socio-economic development pathways (SSPs) are globally agreed among climate experts as standard scenarios (‘Tier 1’) to conduct climate policy research, namely SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0 and SSP5-RCP8.5 (O’Neill et al., 2016). These four standard scenarios were also adopted in our analysis of wheat supply and demand projections. Results The impact of climate and resource availability on wheat yields The results of our fixed effects regression analysis are presented in Table 2. As expected, land, labour and fertilizer were found to significantly contribute to agricultural production. Since wheat yields are higher in irrigated areas than in rain-fed areas, irrigation water was found to have a significantly positive effect on productivity, even though the sample included non-irrigated areas. A 1% increase was associated with a 0.05% increase in 454 M. BABADJANOVA ET AL. production output across the whole sample. However, excluding non-irrigated areas would have resulted in a considerably larger slope coefficient. Temperature also positively contributed to wheat productivity, but only in spring and autumn/fall. A 1% increase in spring temperatures was associated with an increase in crop output by 0.7%, while a 1% increase in autumn temperatures translated into a 0.4% increase in crop output. Conversely, temperature in the summer had a negative effect on productivity as excessive heat dries up the soil and inhibits plant growth. A 1% increase in summer temperatures was associated with a 1.9% drop in production output. The analysis revealed that precipitation levels have no significant impact on productivity, which can be attributed to the fact that the majority of the sample consisted of irrigated areas. Soil salinity was found to have a negative relationship with production, with a 1% increase in soil salinity resulting in a 0.09% decrease in production. Additionally, the study found that groundwater salinity had a positive impact on productivity, as high groundwater salinity is likely a sign for lower water tables and reduced salt accumulation near the plant roots. A 1% increase in groundwater salinity was associated with a 0.11% increase in production. The results were also found to be robust to modifications in the selected time period and to an inclusion of a random effects model (see Table A1 in the supplemental data online). National wheat supply and demand projection under various climatic SSP-RCP pathways Figure 4 displays projections for future wheat supply and demand, considering future temperature and precipitation changes under SSP-RCP scenarios, as well as anticipated shifts in population. The SSP3-RCP7.0 (‘Regional rivalry’) scenario represents strong barriers to climate change mitigation and adaptation and leads to global mean temperature increases of above 5°C. Under this scenario, wheat demand is expected to increase enormously, Table 2. Results of the fixed effect panel model estimates. Variables Dependent variable – total wheat production Coefficient Standard error t-value Ln Land (ha) 0.448*** 0.023 19.3 Ln Labour (1000 UZS) 0.161*** 0.013 11.7 Ln Fertilizer (1000 UZS) 0.388*** 0.023 16.4 Ln Irrig Water(1000 m 3 ) 0.045* 0.022 2.03 Ln Winter Tem (°C) 0.017 0.012 1.41 Ln Spring Tem (°C) 0.697*** 0.263 2.65 Ln Summer Tem (°C) −1.942*** 0.314 −6.18 Ln Autumn Tem (°C) 0.427** 0.163 2.61 Ln Winter Pre (mm) −0.000 0.020 −0.00 Ln Spring Pre (mm) −0.010 0.024 −0.45 Ln Summer Pre (mm) 0.002 0.011 0.20 Ln Autumn Pre (mm) 0.007 0.019 0.37 Ln Groundwater Salinity (gr l −1 ) 0.113*** 0.022 5.02 Ln Soil Salinity (gr l −1 ) −0.092*** 0.021 −4.28 _cons 5.368*** 0.641 8.36 N644 R 2 0.843 Note: *p < 0.05, **p < 0.01, ***p < 0.001. Source: Authors’ calculations. INTERNATIONAL JOURNAL OF WATER RESOURCES DEVELOPMENT 455 Mirzabaev, A. (2013). 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