Your search found 22 records
1 Zacharias, M.; Kumar, S. N.; Singh, S. D.; Rani, D. N. S.; Aggarwal, Pramod Kumar. 2014. Assessment of impacts of climate change on rice and wheat in the Indo-Gangetic plains. Journal of Agrometeorology, 16(1):9-17.
Climate change ; Monsoon climate ; Temperature ; Rain ; Crop modelling ; Crop management ; Rice ; Wheat / India / Pakistan / Bangladesh / Indo-Gangetic Plains
(Location: IWMI HQ Call no: e-copy only Record No: H046907)
In this paper, the climate change scenarios of A2 and B2 for 2070-2100 time scale (denoted as 2080) for several key locations of India and its impact on rice and wheat crops based on regional climate model (PRECIS) were described. The PRECIS projects an increase in temperature over most parts of India especially in the IGP (Indo-Gangetic Plains), the region that presently experiences relatively low temperatures. Extreme high temperature episodes and rainfall intensity days are projected to become more frequent and the monsoon rainfall is also projected to increase. Rabi (mid Nov-March) season is likely to experience higher increase in temperature which could impact and hence become threat to the crops which really require low temperature for their growth. Climatic variability is also projected to increase in both A2 and B2 scenarios. All these projected changes are likely to reduce the wheat and rice yields in Indo-Gangetic plains of India. It is likely that there will be more number of years with low yields occurs towards the end of the century. Such yield reductions in rice and wheat crops due to climate change are mediated through reduction in crop duration, grain number and grain filling duration. The yield loss will be more in A2 scenario compared to B2. These quantitative estimates still have uncertainties associated with them, largely due to uncertainties in climate change projections, future technology growth, availability of inputs such as water for irrigation, changes in crop management and genotype. These projections nevertheless provide a direction of likely change in crop productivity in future climate change scenarios.
2 Khamala, E. 2017. Review of the available remote sensing tools, products, methodologies and data to improve crop production forecasts. Rome, Italy: FAO. 94p.
Remote sensing ; Crop production ; Yield forecasting ; Crop modelling ; Early warning systems ; Drought ; Rain ; Global observing systems ; GIS ; Satellite observation ; Satellite imagery ; Microwave radiation ; Maps ; Statistical data ; Agricultural statistics ; Vegetation index ; Indicators ; National organizations ; Agencies / Africa South of Sahara / Kenya / Senegal / Zimbabwe
(Location: IWMI HQ Call no: e-copy only Record No: H048227)
(1.80 MB) (1.80 MB)
3 Akpoti, K.; Kabo-bah, A. T.; Zwart, Sander J. 2019. Agricultural land suitability analysis: state-of-the-art and outlooks for integration of climate change analysis. Agricultural Systems, 173:172-208. [doi: https://doi.org/10.1016/j.agsy.2019.02.013]
Agricultural land ; Sustainable agriculture ; Sustainable Development Goals ; Land suitability ; Land use ; Integration ; Climate change ; Machine learning ; Crop production ; Crop yield ; Crop modelling ; Food security ; Environmental impact ; Planning ; Water availability ; Socioeconomic environment ; Ecosystems
(Location: IWMI HQ Call no: e-copy only Record No: H049142)
Agricultural land suitability analysis (ALSA) for crop production is one of the key tools for ensuring sustainable agriculture and for attaining the current global food security goal in line with the Sustainability Development Goals (SDGs) of United Nations. Although some review studies addressed land suitability, few of them specifically focused on land suitability analysis for agriculture. Furthermore, previous reviews have not reflected on the impact of climate change on future land suitability and how this can be addressed or integrated into ALSA methods. In the context of global environmental changes and sustainable agriculture debate, we showed from the current review that ALSA is a worldwide land use planning approach. We reported from the reviewed articles 69 frequently used factors in ALSA. These factors were further categorized in climatic conditions (16), nutrients and favorable soils (34 of soil and landscape), water availability in the root zone (8 for hydrology and irrigation) and socio-economic and technical requirements (11). Also, in getting a complete view of crop’s ecosystems and factors that can explain and improve yield, inherent local socio-economic factors should be considered. We showed that this aspect has been often omitted in most of the ALSA modeling with only 38% of the total reviewed article using socio-economic factors. Also, only 30% of the studies included uncertainty and sensitivity analysis in their modeling process. We found limited inclusions of climate change in the application of the ALSA. We emphasize that incorporating current and future climate change projections in ALSA is the way forward for sustainable or optimum agriculture and food security. To this end, qualitative and quantitative approaches must be integrated into a unique ALSA system (Hybrid Land Evaluation System - HLES) to improve the land evaluation approach.
4 Constantin, J.; Raynal, H.; Casellas, E.; Hoffmann, H.; Bindi, M.; Doro, L.; Eckersten, H.; Gaiser, T.; Grosz, B.; Haas, E.; Kersebaum, K.-C.; Klatt, S.; Kuhnert, M.; Lewan, E.; Maharjan, G. R.; Moriondo, M.; Nendel, C.; Roggero, P. P.; Specka, X.; Trombi, G.; Villa, A.; Wang, E.; Weihermuller, L.; Yeluripati, J.; Zhao, Z.; Ewert, F.; Bergez, J.-E. 2019. Management and spatial resolution effects on yield and water balance at regional scale in crop models. Agricultural and Forest Meteorology, 275:184-195. [doi: https://doi.org/10.1016/j.agrformet.2019.05.013]
Crop management ; Crop yield ; Water balance ; Crop modelling ; Crop forecasting ; Strategies ; Evapotranspiration ; Drainage ; Wheat ; Maize / Germany / North Rhine-Westphalia
(Location: IWMI HQ Call no: e-copy only Record No: H049327)
(2.99 MB)
Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia (˜34 083 km²), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 × 1 km to 100 × 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (<10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale.
5 Blanc, E. 2020. Statistical emulators of irrigated crop yields and irrigation water requirements. Agricultural and Forest Meteorology, 284:107828. (Online first) [doi: https://doi.org/10.1016/j.agrformet.2019.107828]
Irrigation water ; Water requirements ; Irrigated farming ; Crop yield ; Water extraction ; Climate change ; Irrigated land ; Crop modelling ; Wheat ; Rice ; Maize ; Soybeans ; Precipitation ; Temperature
(Location: IWMI HQ Call no: e-copy only Record No: H049542)
(18.40 MB)
This study provides statistical emulators of global by gridded crop models included in the Inter-Sectoral Impact Model Intercomparison Project Fast Track project to estimate irrigated crop yields and associated irrigation water withdrawals simulated at the grid cell level. An ensemble of crop model simulations is used to build a panel of monthly summer weather variables and corresponding annual yields and irrigation water withdrawals from five gridded crop models. This dataset is then used to estimate crop-specific response functions for each crop model. The average normalized root mean square errors for the response functions range from 3% to 6% for irrigated yields and 2% to 8% for irrigated water withdrawal. Further in- and out-of-sample validation exercises confirm that the statistical emulators are able to replicate the crop models’ spatial patterns of irrigated crop yields and irrigation water withdrawals, both in levels and in terms of changes over time, although accuracy varies by model and by region. The emulators estimated in this study therefore provide a reliable and computationally efficient alternative to global gridded crop yield models.
6 Jeong, J.; Zhang, X. 2020. Model application for sustainable agricultural water use. Editorial. Agronomy, 10(3):396. (Special issue: Model Application for Sustainable Agricultural Water). [doi: https://doi.org/10.3390/agronomy10030396]
Sustainable agriculture ; Agricultural water use ; Simulation models ; Crop modelling ; Decision support systems ; Agronomy ; Environmental effects ; Uncertainty
(Location: IWMI HQ Call no: e-copy only Record No: H049589)
(0.18 MB) (180 KB)
With the growing population and climate change, increasing demands for water are intensifying competition between agricultural stakeholders. Since the mid-20th century, numerous crop models and modeling techniques have emerged for the quantitative assessment of cropping systems. This article introduces a collection of articles that explore current research in model applications for sustainable agricultural water use. The collection includes articles from model development to regional and field-scale applications addressing management effects, model uncertainty, irrigation decision support systems, and new methods for simulating salt balances. Further work is needed to integrate data science, modern sensor systems, and remote sensing technologies with the models in order to investigate the sustainability of agricultural systems in regions affected by land-use change and climate change.
7 Kamruzzaman, M.; Hwang, S.; Choi, S.-K.; Cho, J.; Song, I.; Song, J.-H.; Jeong, H.; Jang, T.; Yoo, S.-H. 2020. Evaluating the impact of climate change on paddy water balance using APEX-paddy model. Water, 12(3):852. (Special issue: Climate Smart Irrigation Management for Sustainable Agricultural Cultivation) [doi: https://doi.org/10.3390/w12030852]
Climate change ; Water balance ; Rice ; Models ; Crop modelling ; Paddy fields ; Water management ; Irrigation water ; Evapotranspiration ; Forecasting ; Precipitation ; Rain ; Discharges ; Temperature ; Percolation / Republic of Korea
(Location: IWMI HQ Call no: e-copy only Record No: H049645)
(3.62 MB) (3.62 MB)
This research aims to assess the impact of climate change on water balance components in irrigated paddy cultivation. The APEX-Paddy model, which is the modified version of the APEX (Agricultural Policy/Environmental eXtender) model for paddy ecosystems, was used to evaluate the paddy water balance components considering future climate scenarios. The bias-corrected future projections of climate data from 29 GCMs (General Circulation Models) were applied to the APEX-Paddy model simulation. The study area (Jeonju station) forecasts generally show increasing patterns in rainfall, maximum temperature, and minimum temperature with a rate of up to 23%, 27%, and 45%, respectively. The hydrological simulations suggest over-proportional runoff–rainfall and under-proportional percolation and deep-percolation–rainfall relationships for the modeled climate scenarios. Climate change scenarios showed that the evapotranspiration amount was estimated to decrease compared to the baseline period (1976–2005). The evaporation was likely to increase by 0.12%, 2.21%, and 7.81% during the 2010s, 2040s, and 2070s, respectively under Representative Concentration Pathway (RCP)8.5, due to the increase in temperature. The change in evaporation was more pronounced in RCP8.5 than the RCP4.5 scenario. The transpiration is expected to reduce by 2.30% and 12.62% by the end of the century (the 2070s) under RCP4.5 and RCP8.5, respectively, due to increased CO2 concentration. The irrigation water demand is generally expected to increase over time in the future under both climate scenarios. Compared to the baseline, the most significant change is expected to increase in the 2040s by 3.21% under RCP8.5, while the lowest increase was found by 0.36% in 2010s under RCP4.5. The increment of irrigation does not show a significant difference; the rate of increase in the irrigation was found to be greater RCP8.5 than RCP4.5 except in the 2070s. The findings of this study can play a significant role as the basis for evaluating the vulnerability of rice production concerning water management against climate change.
8 Araya, A.; Prasad, P.V.V.; Zambreski, Z.; Gowda, P.H.; Ciampitti, I. A.; Assefa, Y.; Girma, A. 2020. Spatial analysis of the impact of climate change factors and adaptation strategies on productivity of wheat in Ethiopia. Science of The Total Environment, 731:139094. (Online first) [doi: https://doi.org/10.1016/j.scitotenv.2020.139094]
Climate change adaptation ; Strategies ; Agricultural productivity ; Wheat ; Crop yield ; Crop modelling ; Fertilizers ; Nitrogen ; Carbon dioxide ; Irrigation ; Temperature ; Precipitation ; Rain ; Soil types ; Spatial analysis / Ethiopia
(Location: IWMI HQ Call no: e-copy only Record No: H049783)
(4.16 MB)
Wheat production is expected to be challenged by future climate change. However, it is unclear how wheat grown in diverse agroecologies will respond to climate change and adaptation management strategies. A geospatial simulation study was conducted to understand the impacts of climate change and adaptation management strategies on wheat (Triticum aestivum L.) production in Ethiopia. Simulation results showed that the average long-term baseline (1980–2005) wheat yield ranged from 1593 to 3356 kg/ha. This wheat yield range is within the national average (2100–2700 kg/ha) for this decade. In regions with cooler temperatures (<21 °C), mid-century temperatures and elevated CO2, along with increased N fertilizer slightly improved attainable yield levels above 3000 kg/ha. Whereas, in regions with heat and drought conditions wheat yield declined regardless the increase of N or CO2 levels. Wheat yield increased at a diminishing rate with increase in N fertilizer rate. However, N fertilizer did not increase yields under low rainfall conditions. Two to five irrigation per season contributed to yield improvement for low rainfall locations, while yield did not substantially improve for locations receiving adequate seasonal rainfall. Therefore, based on this study, improved N fertilizer application in combination with increased CO2 could improve wheat yield under future climate in most wheat producing regions (with adequate rainfall) of Ethiopia. Our results provide valuable information regarding impacts of climate change factors and adaptation strategies for producers, researchers, extension professionals and policy makers.
9 Kephe, P. N.; Ayisi, K. K.; Petja, B. M. 2021. Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa. Agriculture and Food Security, 10:10. [doi: https://doi.org/10.1186/s40066-020-00283-5]
Crop modelling ; Simulation models ; Climate change adaptation ; Forecasting ; Crop production ; Farm management ; Sustainability ; Weather data ; Decision making ; Remote sensing ; Geographic Information Systems / South Africa
(Location: IWMI HQ Call no: e-copy only Record No: H050470)
https://agricultureandfoodsecurity.biomedcentral.com/track/pdf/10.1186/s40066-020-00283-5.pdf
https://vlibrary.iwmi.org/pdf/H050470.pdf
https://vlibrary.iwmi.org/pdf/H050470.pdf
(1.68 MB) (1.68 MB)
A broad scope of crop models with varying demands on data inputs is being used for several purposes, such as possible adaptation strategies to control climate change impacts on future crop production, management decisions, and adaptation policies. A constant challenge to crop model simulation, especially for future crop performance projections and impact studies under varied conditions, is the unavailability of reliable historical data for model calibrations. In some cases, available input data may not be in the quantity and quality needed to drive most crop models. Even when a suitable choice of a crop simulation model is selected, data limitations hamper some of the models’ effective role for projections. To date, no review has looked at factors inhibiting the effective use of crop simulation models and complementary sources for input data in South Africa. This review looked at the barriers to crop simulation, relevant sources from which input data for crop models can be sourced, and proposed a framework for collecting input data. Results showed that barriers to effective simulations exist because, in most instances, the input data, like climate, soil, farm management practices, and cultivar characteristics, were generally incomplete, poor in quality, and not easily accessible or usable. We advocate a hybrid approach for obtaining input data for model calibration and validation. Recommended methods depending on the intended outputs and end use of model results include remote sensing, field, and greenhouse experiments, secondary data, engaging with farmers to model actual on-farm conditions. Thus, employing more than one method of data collection for input data for models can reduce the challenges faced by crop modellers due to the unavailability of data. The future of modelling depends on the goodness and availability of the input data, the readiness of modellers to cooperate on modularity and standardization, and potential user groups’ ability to communicate.
10 Kelly, T. D.; Foster, T. 2021. AquaCrop-OSPy: bridging the gap between research and practice in crop-water modeling. Agricultural Water Management, 254:106976. [doi: https://doi.org/10.1016/j.agwat.2021.106976]
Crop modelling ; Crop water use ; Optimization methods ; Irrigation scheduling ; Water demand ; Water management ; Climate change ; Soil moisture ; Simulation / USA
(Location: IWMI HQ Call no: e-copy only Record No: H050484)
https://www.sciencedirect.com/science/article/pii/S0378377421002419/pdfft?md5=f0ca8b964b513f3c54f1aeb9868d5e17&pid=1-s2.0-S0378377421002419-main.pdf
https://vlibrary.iwmi.org/pdf/H050484.pdf
https://vlibrary.iwmi.org/pdf/H050484.pdf
(3.48 MB) (3.48 MB)
Crop-growth models are powerful tools for supporting optimal planning and management of agricultural water use globally. However, use of crop models for this purpose often requires advanced programming expertize and computational resources, limiting the potential uptake in integrated water management research by practitioners such as water managers, policymakers, and irrigation service providers. In this article, we present AquaCrop-OSPy (ACOSP), an open source, Python implementation of the crop-water productivity model AquaCrop. The model provides a user friendly, flexible and computationally efficient solution to support agricultural water management, which can be readily integrated with other Python modules or code bases and run instantly via a web browser using the cloud computing platform Google Colab without the need for local installation. This article describes how to run basic simulations using AquaCrop-OSPy, along with more advanced analyses such as optimizing irrigation schedules and evaluating climate change impacts. Each use case is paired with a Jupyter Notebook, which offer an interactive learning environment for users and can be readily adapted to address a range of common irrigation planning and management challenges faced by researcher, policymakers and businesses in both developed and developing countries (https://github.com/thomasdkelly/aquacrop).
11 Silva, J. V.; Pede, V. O.; Radanielson, A. M.; Kodama, W.; Duarte, A.; de Guia, A. H.; Malabayabas, A. J. B.; Pustika, A. B.; Argosubekti, N.; Vithoonjit, D.; Hieu, P. T. M.; Pame, A. R. P.; Singleton, G. R.; Stuart, A. M. 2022. Revisiting yield gaps and the scope for sustainable intensification for irrigated lowland rice in Southeast Asia. Agricultural Systems, 198:103383. [doi: https://doi.org/10.1016/j.agsy.2022.103383]
Irrigated rice ; Sustainable intensification ; Crop yield ; Yield gap ; Lowland ; Food security ; Smallholders ; Crop management ; Cropping systems ; Fertilizers ; Dry season ; Wet season ; Socioeconomic aspects ; Sustainability ; Crop modelling ; Stochastic models / South East Asia / Myanmar / Indonesia / Thailand / Vietnam / Mekong Delta / Bago / Can Tho / Nakhon Sawan / Yogyakarta
(Location: IWMI HQ Call no: e-copy only Record No: H051066)
https://www.sciencedirect.com/science/article/pii/S0308521X22000191/pdfft?md5=29c07ab1e430a194fc17de50b1e72574&pid=1-s2.0-S0308521X22000191-main.pdf
https://vlibrary.iwmi.org/pdf/H051066.pdf
https://vlibrary.iwmi.org/pdf/H051066.pdf
(7.40 MB) (7.40 MB)
CONTEXT: Recent studies on yield gap analysis for rice in Southeast Asia revealed different levels of intensification across the main ‘rice bowls’ in the region. Identifying the key crop management and biophysical drivers of rice yield gaps across different ‘rice bowls’ provides opportunities for comparative analyses, which are crucial to better understand the scope to narrow yield gaps and increase resource-use efficiencies across the region.
OBJECTIVE: The objective of this study was to decompose rice yield gaps into their efficiency, resource, and technology components and to map the scope to sustainably increase rice production across four lowland irrigated rice areas in Southeast Asia through improved crop management.
METHODS: A novel framework for yield gap decomposition accounting for the main genotype, management, and environmental factors explaining crop yield in intensive rice irrigated systems was developed. A combination of crop simulation modelling at field-level and stochastic frontier analysis was applied to household survey data to identify the drivers of yield variability and to disentangle efficiency, resource, and technology yield gaps, including decomposing the latter into its sowing date and genotype components.
RESULTS AND CONCLUSION: The yield gap was greatest in Bago, Myanmar (75% of Yp), intermediate in Yogyakarta, Indonesia (57% of Yp) and in Nakhon Sawan, Thailand (47% of Yp), and lowest in Can Tho, Vietnam (44% of Yp). The yield gap in Myanmar was largely attributed to the resource yield gap, reflecting a large scope to sustainably intensify rice production through increases in fertilizer use and proper weed control (i.e., more output with more inputs). In Vietnam, the yield gap was mostly attributed to the technology yield gap and to resource and efficiency yield gaps in the dry season and wet season, respectively. Yet, sustainability aspects associated with inefficient use of fertilizer and low profitability from high input levels should also be considered alongside precision agriculture technologies for site-specific management (i.e., more output with the same or less inputs). The same is true in Thailand, where the yield gap was equally explained by the technology, resource, and efficiency yield gaps. The yield gap in Indonesia was mostly attributed to efficiency and technology yield gaps and yield response curves to N based on farmer field data in this site suggest it is possible to reduce its use while increasing rice yield (i.e., more output with less inputs).
SIGNIFICANCE: This study provides a novel approach to decomposing rice yield gaps in Southeast Asia's main rice producing areas. By breaking down the yield gap into different components, context-specific opportunities to narrow yield gaps were identified to target sustainable intensification of rice production in the region.
OBJECTIVE: The objective of this study was to decompose rice yield gaps into their efficiency, resource, and technology components and to map the scope to sustainably increase rice production across four lowland irrigated rice areas in Southeast Asia through improved crop management.
METHODS: A novel framework for yield gap decomposition accounting for the main genotype, management, and environmental factors explaining crop yield in intensive rice irrigated systems was developed. A combination of crop simulation modelling at field-level and stochastic frontier analysis was applied to household survey data to identify the drivers of yield variability and to disentangle efficiency, resource, and technology yield gaps, including decomposing the latter into its sowing date and genotype components.
RESULTS AND CONCLUSION: The yield gap was greatest in Bago, Myanmar (75% of Yp), intermediate in Yogyakarta, Indonesia (57% of Yp) and in Nakhon Sawan, Thailand (47% of Yp), and lowest in Can Tho, Vietnam (44% of Yp). The yield gap in Myanmar was largely attributed to the resource yield gap, reflecting a large scope to sustainably intensify rice production through increases in fertilizer use and proper weed control (i.e., more output with more inputs). In Vietnam, the yield gap was mostly attributed to the technology yield gap and to resource and efficiency yield gaps in the dry season and wet season, respectively. Yet, sustainability aspects associated with inefficient use of fertilizer and low profitability from high input levels should also be considered alongside precision agriculture technologies for site-specific management (i.e., more output with the same or less inputs). The same is true in Thailand, where the yield gap was equally explained by the technology, resource, and efficiency yield gaps. The yield gap in Indonesia was mostly attributed to efficiency and technology yield gaps and yield response curves to N based on farmer field data in this site suggest it is possible to reduce its use while increasing rice yield (i.e., more output with less inputs).
SIGNIFICANCE: This study provides a novel approach to decomposing rice yield gaps in Southeast Asia's main rice producing areas. By breaking down the yield gap into different components, context-specific opportunities to narrow yield gaps were identified to target sustainable intensification of rice production in the region.
12 Campana, P. E.; Lastanao, P.; Zainali, S.; Zhang, J.; Landelius, T.; Melton, F. 2022. Towards an operational irrigation management system for Sweden with a water–food–energy nexus perspective. Agricultural Water Management, 271:107734. [doi: https://doi.org/10.1016/j.agwat.2022.107734]
Irrigation management ; Water productivity ; Foods ; Energy consumption ; Nexus approaches ; Drought ; Irrigation systems ; Irrigation water ; Water requirements ; Water conservation ; Crop yield ; Crop modelling ; Evapotranspiration ; Precipitation ; Parameters / Sweden
(Location: IWMI HQ Call no: e-copy only Record No: H051321)
https://www.sciencedirect.com/science/article/pii/S0378377422002815/pdfft?md5=9d243b5d29ac81c719cb9f4c97532ec9&pid=1-s2.0-S0378377422002815-main.pdf
https://vlibrary.iwmi.org/pdf/H051321.pdf
https://vlibrary.iwmi.org/pdf/H051321.pdf
(10.80 MB) (10.8 MB)
The 2018 drought in Sweden prompted questions about climate-adaptation and -mitigation measures – especially in the agricultural sector, which suffered the most. This study applies a water–food–energy nexus modelling framework to evaluate drought impacts on irrigation and agriculture in Sweden using 2018 and 2019 as case studies. A previous water–food–energy nexus model was updated to facilitate an investigation of the benefits of data-driven irrigation scheduling as compared to existing irrigation guidelines. Moreover, the benefits of assimilating earth observation data in the crop model have been explored. The assimilation of leaf area index data from the Copernicus Global Land Service improves the crop yield estimation as compared to default crop model parameters. The results show that the irrigation water productivities of the proposed model are measurably improved compared to conventional and static irrigation guidelines for both 2018 and 2019. This is mostly due to the advantage of the proposed model in providing evapotranspiration in cultural condition (ETc)-driven guidelines by using spatially explicit data generated by mesoscale models from the Swedish Meteorological and Hydrological Institute. During the drought year 2018, the developed model showed no irrigation water savings as compared to irrigation scenarios based on conventional irrigation guidelines. Nevertheless, the crop yield increase from the proposed irrigation management system varied between 10% and 60% as compared to conventional irrigation scenarios. During a normal year, the proposed irrigation management system leads to significant water savings as compared to conventional irrigation guidelines. The modelling results show that temperature stress during the 2018 drought also played a key role in reducing crop yields, with yield reductions of up to 30%. From a water–food–energy nexus, this motivates the implementation of new technologies to reduce water and temperature stress to mitigate likely negative effects of climate change and extremes. By using an open-source package for Google Earth®, a demonstrator of cost-effective visualization platform is developed for helping farmers, and water- and energy-management agencies to better understand the connections between water and energy use, and food production. This can be significant, especially during the occurrence of extreme events, but also to adapt to the negative effects on agricultural production of climate changes.
13 Shoukat, M. R.; Cai, D.; Shafeeque, Muhammad; Habib-ur-Rahman, M.; Yan, H. 2022. Warming climate and elevated CO2 will enhance future winter wheat yields in North China Region. Atmosphere, 13(8):1275. (Special issue: Adaptation for Crop Production and Sustainable Agriculture in a Changing Climate-Volume 2) [doi: https://doi.org/10.3390/atmos13081275]
Climate change adaptation ; Carbon dioxide ; Winter wheat ; Crop yield ; Crop modelling ; Climate models ; Forecasting ; Temperature ; Precipitation ; Irrigation water ; Nitrogen ; Fertilizers ; Socioeconomic development / China / Beijing
(Location: IWMI HQ Call no: e-copy only Record No: H051379)
https://www.mdpi.com/2073-4433/13/8/1275/pdf?version=1660737785
https://vlibrary.iwmi.org/pdf/H051379.pdf
https://vlibrary.iwmi.org/pdf/H051379.pdf
(13.70 MB) (13.7 MB)
The projected climate change substantially impacts agricultural productivity and global food security. The cropping system models (CSM) can help estimate the effects of the changing climate on current and future crop production. The current study evaluated the impact of a projected climate change under shared socioeconomic pathways (SSPs) scenarios (SSP2-4.5 and SSP5-8.5) on the grain yield of winter wheat in the North China Plain by adopting the CSM-DSSAT CERES-Wheat model. The model was calibrated and evaluated using observed data of winter wheat experiments from 2015 to 2017 in which nitrogen fertigation was applied to various growth stages of winter wheat. Under the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) SSP2-4.5 and SSP5-8.5 scenarios, the future climate projections were based on five global climate models (GCMs) of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The GCMs projected an increase in grain yield with increasing temperature and precipitation in the near-term, mid-term, and long-term projections. In the mid-term, 13% more winter wheat grain yield is predicted under 1.3 C, and a 33 mm increase in temperature and precipitation, respectively, compared with the baseline period (1995–2014). The increasing CO2 concentration trends projected an increase in average grain yield from 4 to 6%, 4 to 14%, and 2 to 34% in the near-term, mid-term, and long-term projections, respectively, compared to the baseline. The adaptive strategies were also analyzed, including three irrigation levels (200, 260, and 320 mm), three nitrogen fertilizer rates (275, 330, and 385 kg ha-1 ), and four sowing times (September 13, September 23, October 3, and October 13). An adaptive strategy experiments indicated that sowing winter wheat on October 3 (traditional planting time) and applying 275 kg ha-1 nitrogen fertilizer and 260 mm irrigation water could positively affect the grain yield in the North China Plain. These findings are beneficial in decision making to adopt and implement the best management practices to mitigate future climate change impacts on wheat grain yields.
14 Yan, W.; Li, F.; Zhao, Y. 2022. Determination of irrigation water quantity and its impact on crop yield and groundwater. Agricultural Water Management, 273:107900. (Online first) [doi: https://doi.org/10.1016/j.agwat.2022.107900]
Irrigation water ; Crop yield ; Groundwater table ; Irrigation schemes ; Water use ; Water demand ; Crop modelling ; Maize ; Water reservoirs ; Surface water ; Soil water content ; Precipitation / China / Shijin Irrigation District / Huangbizhuang Reservoir
(Location: IWMI HQ Call no: e-copy only Record No: H051353)
(2.12 MB)
The objective of reservoir water allocation based on considering the ecological water demand of the downstream river has an important impact on the water allocation of each water user and the downstream ecology. Agricultural irrigation with large water consumption will directly affect crop yield. Irrigation water, as the recharge of groundwater in irrigation districts, also plays an important role in the restoration of groundwater. This research used the range of variability approach (RVA), a method of flow management considering the water demand of river ecosystems, to provide the recommended outflow range (RVA target) of the Huangbizhuang Reservoir upstream of the Shijin irrigation district. First, we used the RVA target to determine the water allocation of each water user, set 27 irrigation schemes with the water allocation of agriculture as a constraint, and used the validated AquaCrop model (seven years of field experiment data were used to calibrate AquaCrop model parameters) to evaluate the most appropriate irrigation schemes and their impact on groundwater restoration in the irrigation district. The results showed that the amount of water available for agricultural irrigation in the flood season under the RVA target was 84 mm, which was 36 mm less than the current irrigation quota (120 mm). Three irrigation schemes better than the current scheme (scheme 0) were selected, i.e., scheme 1 (irrigate 42 mm at the seedling and jointing stages), scheme 16 (irrigate 42 mm at the seedling stage), and scheme 22 (irrigate 84 mm at the seedling stage). Scheme 22 increased the yield of corn and WP (water productivity), which were 17 kg/ha and 1 kg/ha/mm higher than scheme 0, respectively, which provided the greatest increase in yield. Scheme 22 can restore 0.727 m of the groundwater table. The yield increased in scheme 16 was 4 kg/ha higher than that in scheme 0. Scheme 16 can restore 0.982 m of the groundwater table, which was the most conducive scheme for groundwater recharge. The WP of scheme 16 and scheme 1 was slightly different from that of scheme 0. There was a slight difference in biomass among the four schemes. The yield increased in scheme 1 was 12 kg/ha higher than that in scheme 0, which could restore 0.727 m of the groundwater table simultaneously, which was for a normal year. This research can provide a reference for the formulation of a local irrigation scheme to stabilize and increase summer maize yield on the premise of satisfying the ecological water demand of the river and restoring the groundwater table.
15 Kaini, S.; Harrison, M. T.; Gardner, T.; Nepal, Santosh; Sharma, A. K. 2022. The impacts of climate change on the irrigation water demand, grain yield, and biomass yield of wheat crop in Nepal. Water, 14(17):2728. (Special issue: How Does Agricultural Water Resources Management Adapt to Climate Change?) [doi: https://doi.org/10.3390/w14172728]
Climate change ; Irrigation water ; Water demand ; Crop yield ; Biomass ; Wheat ; Cropping systems ; Irrigation schemes ; Irrigation management ; Crop modelling ; Forecasting ; Water requirements ; Extreme weather events ; Farmers / Nepal / Sunsari Morang Irrigation Scheme
(Location: IWMI HQ Call no: e-copy only Record No: H051485)
https://www.mdpi.com/2073-4441/14/17/2728/pdf?version=1662382187
https://vlibrary.iwmi.org/pdf/H051485.pdf
https://vlibrary.iwmi.org/pdf/H051485.pdf
(4.81 MB) (4.81 MB)
The Nepalese Sunsari Morang Irrigation district is the lifeblood of millions of people in the Koshi River basin. Despite its fundamental importance to food security, little is known about the impacts of climate change on future irrigation demand and grain yields in this region. Here, we examined the impacts of climate change on the irrigation demand and grain yield of wheat crop. Climate change was simulated using Representative Concentration Pathways (RCPs) of 4.5 and 8.5 for three time horizons (2016–2045, 2036–2065, and 2071–2100) in the Agricultural Production Systems Simulator (APSIM). For the field data’s measured period (2018–2020), we showed that farmers applied only 25% of the irrigation water required to achieve the maximum potential grain yield. Actual yields were less than 50% of the potential yields. Projected irrigation water demand is likely to increase for RCP4.5 (3%) but likely to decrease under RCP8.5 (8%) due to the truncated crop duration and lower maturity biomass by the end of the 21st century. However, simulated yields declined by 20%, suggesting that even irrigation will not be enough to mitigate the severe and detrimental effects of climate change on crop production. While our results herald positive implications for irrigation demand in the region, the implications for regional food security may be dire.
16 Chimonyo, V. G. P.; Chibarabada, T. P.; Choruma, D. J.; Kunz, R.; Walker, S.; Massawe, F.; Modi, A. T.; Mabhaudhi, Tafadzwanashe. 2022. Modelling neglected and underutilised crops: a systematic review of progress, challenges, and opportunities. Sustainability, 14(21):13931. (Special issue: Interdisciplinary Approaches to Mainstreaming Underutilized Crops) [doi: https://doi.org/10.3390/su142113931]
(Location: IWMI HQ Call no: e-copy only Record No: H051496)
https://www.mdpi.com/2071-1050/14/21/13931/pdf?version=1666790014
https://vlibrary.iwmi.org/pdf/H051496.pdf
https://vlibrary.iwmi.org/pdf/H051496.pdf
(0.91 MB) (930 KB)
Developing and promoting neglected and underutilised crops (NUS) is essential to building resilience and strengthening food systems. However, a lack of robust, reliable, and scalable evidence impedes the mainstreaming of NUS into policies and strategies to improve food and nutrition security. Well-calibrated and validated crop models can be useful in closing the gap by generating evidence at several spatiotemporal scales needed to inform policy and practice. We, therefore, assessed progress, opportunities, and challenges for modelling NUS using a systematic review. While several models have been calibrated for a range of NUS, few models have been applied to evaluate the growth, yield, and resource use efficiencies of NUS. The low progress in modelling NUS is due, in part, to the vast diversity found within NUS that available models cannot adequately capture. A general lack of research compounds this focus on modelling NUS, which is made even more difficult by a deficiency of robust and accurate ecophysiological data needed to parameterise crop models. Furthermore, opportunities exist for advancing crop model databases and knowledge by tapping into big data and machine learning.
17 Mehla, M. K.; Kothari, M.; Singh, P. K.; Bhakar, S. R.; Yadav, K. K. 2022. Assessment of water footprint for a few major crops in Banas River Basin of Rajasthan. Journal of Applied and Natural Science, 14(4):1264-1271. [doi: https://doi.org/10.31018/jans.v14i4.3896]
Water footprint ; Crop production ; Barley ; Wheat ; Rice ; Millets ; Cotton ; Soybeans ; Pearl millet ; Chickpeas ; Water use ; Water scarcity ; Water productivity ; Crop modelling / India / Rajasthan / Banas River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051598)
https://journals.ansfoundation.org/index.php/jans/article/view/3896/2357
https://vlibrary.iwmi.org/pdf/H051598.pdf
https://vlibrary.iwmi.org/pdf/H051598.pdf
(0.81 MB) (828 KB)
Water security is essential for socio-economic development, ecosystem management, and environmental sustainability. An improved understanding of the relationships between water demand and supply is needed to mitigate the impacts of diminishing water resources. The present study aimed to assess the crop water footprint of sixteen major crops in the basin namely, bajra/ pearl millet (Pennisetum glaucum L.), barley (Hordeum vulgare L.), cotton (Gossypium herbaceum L.), gram/chickpea (Cicer arietinum L.), groundnut (Arachis hypogaea L.), guar/cluster beans (Cyamopsis tetragonoloba L.), jowar/ sorghum (Sorghum bicolor L.), lentil/ masoor (Lens culinaris L.), maize (Zea mays L.), mungbean (Vigna radiata L.), rapeseed & mustard (Brassica napus L.), rice/paddy (Oryza sativa L.), sesame (Sesamum indicum L.), soybean (Glycine max L.), urad/ black gram (Vigna mungo L.) and wheat (Triticum aestivum L.) was estimated during 2008-2020 in the Banas river basin of Rajasthan. The average annual water footprint of crop production varied from 11365.8-23131.5 MCM/yr (Mean 19254.5 MCM/yr) during the study period. Wheat, bajra, maize, rapeseed & mustard make up 67.4 % of the total average annual water footprint of crop production. The blue water footprint of crop production was 3942.1 MCM/yr, with wheat, rapeseed & mustard accounting for almost 87.0 % of the average annual blue water footprint. Blue, green and grey water footprints comprised 20.8, 69.7 and 9.5 % of the total WF of crop production in the basin, respectively. This assessment can play a significant role in developing better policies for properly managing water footprints for sustainable crop production in the basin.
18 Serra, J.; Paredes, P.; Cordovil, C.; Cruz, S.; Hutchings, N.; Cameira, M. 2023. Is irrigation water an overlooked source of nitrogen in agriculture? Agricultural Water Management, 278:108147. (Online first) [doi: https://doi.org/10.1016/j.agwat.2023.108147]
Irrigation water ; Nitrogen ; Nutrient management ; Water management ; Fertilizers ; Surface water ; Water storage ; Evapotranspiration ; Irrigation systems ; Water requirements ; Soil water ; Crop yield ; Biomass ; Precipitation ; Crop modelling ; Wheat ; Maize ; Potatoes ; Tomatoes ; Rice ; Policies ; Irrigation requirements ; Sprinklers ; Groundwater table ; Indicators ; Water quality ; Water productivity
(Location: IWMI HQ Call no: e-copy only Record No: H051602)
https://www.sciencedirect.com/science/article/pii/S0378377423000124/pdfft?md5=3eead5852e7b50db297647ba3cd26036&pid=1-s2.0-S0378377423000124-main.pdf
https://vlibrary.iwmi.org/pdf/H051602.pdf
https://vlibrary.iwmi.org/pdf/H051602.pdf
(15.60 MB) (15.6 MB)
The increase of agricultural nitrogen (N) inputs since the 1960s is a key driver in surface- and groundwater nitrate pollution. The water abstracted from these sources can input substantial amounts of reactive nitrogen (NIrrig) if used for crop irrigation. This input is often not included in N related agricultural policies and studies, which are likely underestimating the magnitude of N pollution hotspots and overestimating the N use efficiency. In this study, we provided prima facie evidence that NIrrig is a neglected source of N in irrigated systems. The NIrrig was computed for 278 municipalities in mainland Portugal along the period 1995–2019 based on the gross irrigation requirements and nitrate concentration in ground- and surface water sources. The former was derived using two complementary approaches, using the AquaCrop and GlobWat models, while the latter were computed following spatially explicit approaches. NIrrig showed annual large fluctuations (6–11 Gg N yr-1), of which 91% was from groundwater sources. Results show that NIrrig averaged 14 ( ± 11) kg N ha-1 yr-1, which is equivalent to 3 ( ± 4) % of the N in synthetic fertilisers. This input was higher in the municipalities that simultaneously present high irrigation demand and the nitrate-contaminated groundwater as an irrigation source. In these cases, located in Nitrate Vulnerable Zones, NIrrig reached up to 95 kg N ha-1 yr-1 and more than 80% of the N in synthetic fertilizers. This study highlights the importance of linking water and nutrient policies to better gain insight on NIrrig, for which the current study provided for a simple modelling framework.
19 Bhatti, Muhammad Tousif; Anwar, A. A.; Hussain, Kashif. 2023. Characterization and outlook of climatic hazards in an agricultural area of Pakistan. Scientific Reports, 13:9958. [doi: https://doi.org/10.1038/s41598-023-36909-4]
Climate change ; Weather hazards ; Climate prediction ; Temperature ; Precipitation ; Drought ; Rainfall ; Disaster preparedness ; Crop yield ; Crop modelling / Pakistan / Khyber Pakhtunkhwa / Gomal Zam Dam Command Area
(Location: IWMI HQ Call no: e-copy only Record No: H052080)
(10.20 MB) (10.2 MB)
Many dimensions of human life and the environment are vulnerable to anthropogenic climate change and the hazards associated with it. There are several indices and metrics to quantify climate hazards that can inform preparedness and planning at different levels e.g., global, regional, national, and local. This study uses biased corrected climate projections of temperature and precipitation to compute characteristics of potential climate hazards that are pronounced in the Gomal Zam Dam Command Area (GZDCA)— an irrigated agricultural area in Khyber Pakhtunkhwa province of Pakistan. The results answer the question of what the future holds in the GZDCA regarding climate hazards of heatwaves, heavy precipitation, and agricultural drought. The results of heatwaves and agricultural drought present an alarming future and call for immediate actions for preparedness and adaptation. The magnitude of drought indices for the future is correlated with the crop yield response based on AquaCrop model simulations with observed climate data being used as input. This correlation provides insight into the suitability of various drought indices for agricultural drought characterization. The results elaborate on how the yield of wheat crop grown in a typical setting common in the South Asian region respond to the magnitude of drought indices. The findings of this study inform the planning process for changing climate and expected climate hazards in the GZDCA. Analyzing climate hazards for the future at the local level (administrative districts or contiguous agricultural areas) might be a more efficient approach for climate resilience due to its specificity and enhanced focus on the context.
20 Alvar-Beltran, J.; Soldan, R.; Vanuytrecht, E.; Heureux, A.; Shrestha, Nirman; Manzanas, R.; Pant, K. P.; Franceschini, G. 2023. An FAO model comparison: Python Agroecological Zoning (PyAEZ) and AquaCrop to assess climate change impacts on crop yields in Nepal. Environmental Development, 47:100882. [doi: https://doi.org/10.1016/j.envdev.2023.100882]
Crop modelling ; Agroecological zones ; Climate change ; Crop yield ; Maize ; Rice ; Wheat ; Irrigation management ; Rainfed farming ; Water productivity ; Soil texture ; Temperature ; River basins ; FAO / Nepal / Koshi River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H052082)
(9.28 MB)
To identify the most effective agricultural transformation and adaptation measures, the Food and Agriculture Organization (FAO) calls for action to produce robust crop suitability assessments. We developed a novel approach to assess the inputs and outputs of two FAO tools (AEZ and AquaCrop). We use Nepal as a case study, a country offering a myriad of ecoclimatic conditions for multiple crops. Our work provides further evidence of climate change impacts on rice, maize and wheat yields along the different agroclimatic zones of Nepal, equally under rainfed and irrigated conditions for future climate scenarios. The findings of bias-adjusted regional climate models (RCMs) shows increasing temperatures and precipitation; whereas the outputs of agroecological/crop models show effective adaptation of C3 crops to a CO2 enriched environment. In sum, this supports the climate-crop modelling user community, extension workers and government agencies with guidance’s to overcome uncertainties associated with the application of these tools.
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