Your search found 16 records
1 Wani, S. P.; Rockstrom, J.; Oweis, T. (Eds.) 2009. Rainfed agriculture: unlocking the potential. Wallingford, UK: CABI; Patancheru, Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Colombo, Sri Lanka: International Water Management Institute (IWMI) 310p. (Comprehensive Assessment of Water Management in Agriculture Series 7)
Rainfed farming ; Soil degradation ; Crop production ; Climate change ; Irrigation methods ; Water harvesting ; Yield gap ; Models ; Supplemental irrigation ; Water productivity ; Watershed management / India
(Location: IWMI HQ Call no: IWMI 631.586 G000 WAN Record No: H041989)
http://www.iwmi.cgiar.org/Publications/CABI_Publications/CA_CABI_Series/Rainfed_Agriculture/Protected/Rainfed_Agriculture_Unlocking_the_Potential.pdf
http://vlibrary.iwmi.org/pdf/H041989_TOC.pdf
http://vlibrary.iwmi.org/pdf/H041989_TOC.pdf
(7.62MB)
2 Singh, P.; Aggarwal, P. K.; Bhatia, V. S.; Murty, M. V. R.; Pala, M.; Oweis, T.; Benli, B.; Rao, K. P. C.; Wani, S. P. 2009. Yield gap analysis: modelling of achievable yields at farm level. In Wani, S. P.; Rockstrom, J.; Oweis, T. (Eds.). Rainfed agriculture: unlocking the potential. Wallingford, UK: CABI; Patancheru, Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Colombo, Sri Lanka: International Water Management Institute (IWMI) pp.81-123. (Comprehensive Assessment of Water Management in Agriculture Series 7)
Yield gap ; Analysis ; Cereals ; Rainfed farming ; Crop yield ; Oilseeds ; Crop production / Asia / Africa / India / Thailand / Vietnam / Syria / South Africa / Morocco / Niger / Kenya / Zimbabwe
(Location: IWMI HQ Call no: IWMI 631.586 G000 WAN Record No: H041995)
3 Wani, S. P.; Rockstrom, J.; Oweis, T. (Eds.) 2009. Rainfed agriculture: unlocking the potential. Wallingford, UK: CABI; Patancheru, Andhra Pradesh, India: International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Colombo, Sri Lanka: International Water Management Institute (IWMI) 310p. (Comprehensive Assessment of Water Management in Agriculture Series 7)
Rainfed farming ; Soil degradation ; Crop production ; Climate change ; Irrigation methods ; Water harvesting ; Yield gap ; Models ; Supplemental irrigation ; Water productivity ; Watershed management / India
(Location: IWMI HQ Call no: IWMI 631.586 G000 WAN c2 Record No: H042126)
http://www.iwmi.cgiar.org/Publications/CABI_Publications/CA_CABI_Series/Rainfed_Agriculture/Protected/Rainfed_Agriculture_Unlocking_the_Potential.pdf
http://vlibrary.iwmi.org/pdf/H041989_TOC.pdf
http://vlibrary.iwmi.org/pdf/H041989_TOC.pdf
4 Amarasinghe, Upali; Malik, Ravinder Paul Singh; Sharma, Bharat R. 2010. Overcoming growing water scarcity: exploring potential improvements in water productivity in India. Natural Resources Forum, 34:188-199.
Water scarcity ; Water productivity ; Water deficit ; Rainfed farming ; Supplemental irrigation ; Yield gap / India
(Location: IWMI HQ Call no: e-copy only Record No: H043093)
(0.19 MB)
Improvements in water productivity (WP) are often suggested as one of the alternative strategies for overcoming growing water scarcity in India. This paper explores the potential improvements in WP of food grains at district level, which currently varies between 0.11 and 1.01 kilogram per cubic metre (kg/m3), in the 403 districts that account for 98% of the total production of food grains. The paper first finds the maximum yield function conditional on consumptive water use (CWU) and then explores the potential improvements in WP by: (a) bridging the gap between actual and maximum yield while keeping CWU constant; and (b) changing the maximum yield by adjusting the CWU using supplementary or deficit irrigation. Deficit irrigation in some areas may decrease yield but can increase production if land availability is not a constraint. A large potential exists for bridging the yield gap in irrigated areas with CWU between 300 and 475 mm. Of the 222 districts that fall under this category, a 50% reduction in yield gap alone could increase production by 100 million tonnes (Mt) without increasing CWU. Supplementary irrigation can increase yield and WP in rain-fed and irrigated areas of 266 and 16 districts with CWU is below 300 mm. Deficit irrigation in irrigated areas of 185 districts with CWU above 475 mm could increase yield, WP and production. Decreasing CWU in irrigated areas with CWU between 425 and 475 mm reduces yield slightly, but if availability of land is not a constraint then the benefits due to water saving and production increases could exceed the cost.
5 Johnston, Robyn; Hoanh, Chu Thai; Lacombe, Guillaume; Lefroy, R.; Pavelic, Paul; Fry, C. 2012. Improving water use in rainfed agriculture in the Greater Mekong Subregion. Summary report. [Summary report of the Project report prepared by IWMI for Swedish International Development Agency (Sida)]. Colombo, Sri Lanka: International Water Management Institute (IWMI); Stockholm, Sweden: Swedish International Development Cooperation Agency (Sida) 44p. [doi: https://doi.org/10.5337/2012.200]
Water management ; Water use ; Groundwater ; Rainfed farming ; Irrigated farming ; Agricultural production ; Crops ; Rice ; Yield gap ; Environmental effects ; Case studies ; Deltas ; Reservoirs ; Farm ponds ; Farmers ; Agroecology ; Climate change ; Drought / Southeast Asia / Cambodia / Laos / Myanmar / Thailand / Vietnam / China / Greater Mekong Subregion
(Location: IWMI HQ Call no: IWMI Record No: H044801)
(3.18MB)
6 Pala, M.; Oweis, T.; Benli, B.; De Pauw, E.; El Mourid, M.; Karrou, M.; Jamal, M.; Zencirci, N. 2011. Assessment of wheat yield gap in the Mediterranean: case studies from Morocco, Syria and Turkey. Aleppo, Syria: International Center for Agricultural Research in the Dry Areas (ICARDA). 36p.
Rainfed farming ; Crop management ; Wheat ; Yield gap ; Assessment ; Case studies ; Agroecology ; Land management / Morocco / Syria / Turkey
(Location: IWMI HQ Call no: 630 G000 PAL Record No: H044940)
http://www.icarda.org/docrep/Reports/Assessment_of_wheat.pdf
https://vlibrary.iwmi.org/pdf/H044940.pdf
https://vlibrary.iwmi.org/pdf/H044940.pdf
(2.05 MB) (2.3MB)
7 University of Nebraska. Lincoln Office of Research and Economic Development. 2012. Paths to solutions: proceedings of the 2011 Water for Food Conference, Lincoln, USA, 1-4 May 2011. Lincoln, NE, USA: University of Nebraska. 129p.
Water management ; Water scarcity ; Food security ; Food production ; Developing countries ; Climate change ; Drip irrigation ; Smallholders ; Farmers ; Water productivity ; Water use efficiency ; Yield gap ; Aquifers ; Institutions
(Location: IWMI HQ Call no: 333.91 G000 UNI Record No: H045114)
http://waterforfood.nebraska.edu/wp-content/uploads/2012/02/wff2011_fullversion.pdf
https://vlibrary.iwmi.org/pdf/H045114.pdf
https://vlibrary.iwmi.org/pdf/H045114.pdf
(12.36 MB) (1.83MB)
8 Kuppannan, Palanisami; Das, A. 2013. Water management options in the hill regions of Uttarakhand [India]. In Palanisami, Kuppannan; Sharda, V. N.; Singh, D. V. (Eds.). Water management in the hill regions: evidence from field studies. [Outcome of the IWMI and ICAR Workshop organized by IWMI-TATA Water Policy Research Program]. New Delhi, India: Bloomsbury Publishing India. pp.72-94.
Highlands ; Water management ; Water resources ; Climatic zones ; Rain ; Drainage ; Agricultural production ; Yield gap ; Irrigated land ; Irrigation systems ; Supplemental irrigation ; Microirrigation ; Legal aspects ; Economic aspects ; Costs ; Research programmes / India / Uttarakhand
(Location: IWMI HQ Call no: 333.91 G635 PAL Record No: H045729)
(1.36 MB)
9 Johnson, M.; Benin, S.; You, L.; Diao, X.; Chilonda, Pius. 2014. Exploring strategic priorities for regional agricultural research and development investments in Southern Africa. Washington, DC, USA: International Food Policy Research Institute (IFPRI). 140p. (IFPRI Discussion Paper 01318)
Agricultural research ; Agricultural development ; Investment ; Economic growth ; Indicators ; Markets ; Models ; Yield gap ; Cereal crops ; Farming systems ; Livestock products / Southern Africa
(Location: IWMI HQ Call no: e-copy only Record No: H046297)
http://www.ifpri.org/sites/default/files/publications/ifpridp01318.pdf
https://vlibrary.iwmi.org/pdf/H046297.pdf
https://vlibrary.iwmi.org/pdf/H046297.pdf
(4.27 MB) (4.27 MB)
An in-depth quantitative analysis is undertaken in this paper to assist the Southern African Development Community (SADC) Secretariat, member countries, and development partners in setting future regional investment priorities for agricultural research and development in the SADC region. A primary goal of this work was to identify a range of agricultural research priorities for achieving sector productivity and overall economic growth in southern Africa, at both the country and regional levels. This is accomplished by adopting an integrated modeling framework that combines a disaggregated spatial analytical model with an economywide multimarket model developed specifically for the region. The spatial disaggregation uses information on current yield gaps to project growth and technology spillovers across countries among different agricultural activities that share similar conditions and thus potential for adoption and diffusion in the region. The economywide multimarket model is used to simulate ex ante the economic effects of closing these yield gaps through a country’s own investments in research and development (R&D) and from potential R&D spill-ins from neighboring countries. Results indicate a high potential of spillovers and technology adaptability across countries due to similar agroecological and climatic conditions and the countries’ own capacities for adaptive R&D. The greatest agriculture-led growth opportunities reside in staple crops and in roots and tubers, especially among the low-income countries. Together, these sectors have the potential to contribute up to 40 percent of future possible growth. There are differences (areas of comparative advantage) at the country level that offer opportunities for specialization. For example, grains are the dominant subsector for Zimbabwe; in Botswana, opportunities will depend on more growth in its livestock sector; and for Namibia promoting fish growth may be more important. The root crops sector is as important as that of grains in Angola, Democratic Republic of the Congo, and Malawi, but even more important in Mozambique. The study finds evidence of high spillover potential, especially for maize, rice, cattle, cassava, sorghum, and beans. Low-income countries gain the most from spill-in of R&D in the grains and roots subsectors; yield growth in these subsectors explains about 20 percent of these countries’ gains in the total value of production, compared with only 2.2 percent among middle-income countries. Our results emphasize not only the importance of expanding regional cooperation in R&D and technology diffusion in southern Africa, but the importance of strengthening regional agricultural markets and linkages with nonagricultural sectors.
10 Sadras, V. O.; Cassman, K. G.; Grassini, P.; Hall, A. J.; Bastiaanssen, W. G. M.; Laborte, A. G.; Milne, A. E.; Sileshi, G.; Steduto, P. 2015. Yield gap analysis of field crops: methods and case studies. Rome, Itlay: FAO. (FAO Water Reports 41)
Yield gap ; Field crops ; Crop yield ; Cropping systems ; Environmental effects ; Water productivity ; Water availability ; Weather data ; Rainfed farming ; Irrigation systems ; Maize ; Rice ; Grain legumes ; Quinoa ; Nitrogen fertilizers ; Soil fertility ; Remote sensing ; Case studies / Argentina / Africa South of Sahara / India / China / USA / Southeast Asia / Zimbabwe / Bolivia
(Location: IWMI HQ Call no: 631.558 G000 SAD Record No: H047614)
(5.72 MB)
11 Dixon, J.; Garrity, D. P.; Boffa, J.-M.; Williams, Timothy Olalekan; Amede, T.; Auricht, C.; Lott, R.; Mburathi, G. (Eds.) 2020. Farming systems and food security in Africa: priorities for science and policy under global change. Oxon, UK: Routledge - Earthscan. 638p. (Earthscan Food and Agriculture Series)
Farming systems ; Food security ; Climate change ; Policies ; Urban agriculture ; Peri-urban agriculture ; Sustainable development ; Irrigated farming ; Large scale systems ; Mixed farming ; Agropastoral systems ; Perennials ; Agricultural productivity ; Intensification ; Diversification ; Farm size ; Land tenure ; Livestock ; Fish culture ; Agricultural extension ; Forests ; Highlands ; Drylands ; Fertilizers ; Soil fertility ; Water management ; Natural resources ; Nutrition security ; Energy ; Technology ; Investment ; Market access ; Trade ; Human capital ; Agricultural population ; Gender ; Women ; Smallholders ; Farmers ; Living standards ; Poverty ; Hunger ; Socioeconomic environment ; Households ; Yield gap ; Tree crops ; Tubers ; Cereal crops ; Root crops ; Maize ; Ecosystem services ; Resilience ; Strategies / Africa South of Sahara / West Africa / East Africa / Southern Africa / Central Africa / Middle East / North Africa / Sahel
(Location: IWMI HQ Call no: e-copy only Record No: H049739)
(103 MB)
12 Dixon, J.; Boffa, J.-M.; Williams, Timothy Olalekan; de Leeuw, J.; Fischer, G.; van Velthuizen, H. 2020. Farming and food systems potentials. In Dixon, J.; Garrity, D. P.; Boffa, J.-M.; Williams, Timothy Olalekan; Amede, T.; Auricht, C.; Lott, R.; Mburathi, G. (Eds.). Farming systems and food security in Africa: priorities for science and policy under global change. Oxon, UK: Routledge - Earthscan. pp.535-561. (Earthscan Food and Agriculture Series)
Farming systems ; Food systems ; Food security ; Nutrition security ; Agricultural productivity ; Yield gap ; Intensification ; Diversification ; Agricultural population ; Farmers ; Farm size ; Nonfarm income ; Livestock ; Market access ; Poverty ; Households ; Living standards ; Labour mobility ; Strategies ; Institutions ; Policies ; Technology ; Natural resources management ; Ecosystem services / Sahel / Africa South of Sahara / West Africa / East Africa / Southern Africa / Central Africa / Middle East / North Africa
(Location: IWMI HQ Call no: e-copy only Record No: H049741)
http://old.worldagroforestry.org/downloads/Publications/PDFS/BC20009.pdf
https://vlibrary.iwmi.org/pdf/H049741.pdf
https://vlibrary.iwmi.org/pdf/H049741.pdf
(0.18 MB) (181 KB)
13 Montanaro, G.; Nangia, V.; Gowda, P.; Mukhamedjanov, S.; Mukhamedjanov, A.; Haddad, M.; Yuldashev, Tulkun; Wu, W. 2021. Heat units-based potential yield assessment for cotton production in Uzbekistan. International Journal of Agricultural and Biological Engineering, 14(6):137-144. [doi: https://doi.org/10.25165/j.ijabe.20211406.4803]
Cotton ; Crop production ; Crop yield ; Yield gap ; Yield potential ; Assessment ; Heat units ; Climate variability ; Agriculture / Uzbekistan
(Location: IWMI HQ Call no: e-copy only Record No: H050907)
https://ijabe.org/index.php/ijabe/article/download/4803/pdf
https://vlibrary.iwmi.org/pdf/H050907.pdf
https://vlibrary.iwmi.org/pdf/H050907.pdf
(0.77 MB) (792 KB)
Cotton yields in Uzbekistan are significantly lower than those in similar agro-climatic regions, requiring the estimation of crop potential and baseline yield to track progress of production enhancement efforts. The current study estimated potential cotton development and baseline yield (maximum given no production constraints) using total heat units (THU) and potential cotton yield (PCY), respectively. Calculations were based on heat units (HU) for a 30-year (1984-2013) period. Long-term average THU and PCY, as well as PCY at three different exceedance probabilities (p=0.99, p=0.80, and p=0.75), were calculated for 21 selected weather stations across cotton-growing areas of Uzbekistan. After confirmation that the current planting date (April 15) is optimal, a comparison of THU with the accepted cotton production cutoff threshold (1444°C) suggested that areas with lower elevations and latitudes are more appropriate for cotton production. Yield gap analysis (relative difference between long-term average PCY and actual yields) confirmed that Uzbekistan cotton production is below potential, while the spatial distribution of yield gaps outlined where efforts should be targeted. Areas near the stations of Nukus, Kungrad, Chimbay, and Syrdarya should be further investigated as benefit/cost ratio is highest in these areas. A comparison between state-set yield targets and PCY values, taking into account climatic variability, suggested that all areas except Jaslyk, Nurata, and Samarkand have safe, appropriate targets. These results present a starting-point to aid in strategic actions for Uzbekistan cotton production improvement.
14 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.
15 Alam, Mohammad Faiz; Durga, Neha; Sikka, Alok; Verma, Shilp; Mitra, Archisman; Amarasinghe, Upali; Mahapatra, Smaranika. 2022. Agricultural Water Management (AWM) typologies: targeting land-water management interventions towards improved water productivity. New Delhi, India: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH; New Delhi, India: International Water Management Institute (IWMI). 29p.
Agriculture ; Water management ; Land ; Water productivity ; Yield gap ; Water availability ; Water security ; Groundwater ; Aquifers ; Water conservation ; Watershed management / India
(Location: IWMI HQ Call no: e-copy only Record No: H051383)
(33.7 MB)
16 Tomasella, J.; Martins, M. A.; Shrestha, Nirman. 2023. An open-source tool for improving on-farm yield forecasting systems. Frontiers in Sustainable Food Systems, 7:1084728. [doi: https://doi.org/10.3389/fsufs.2023.1084728]
Yield forecasting ; Crop forecasting ; Soil fertility ; Irrigation management ; Yield gap ; Crop modelling ; Optimization ; On-farm research ; Wheat ; Maize ; Soil water content ; Water productivity ; Biomass ; Canopy ; Climate change ; Assessment ; Computer software / Tunisia / Nepal / Brazil / Tunis / Chitwan / Araripina
(Location: IWMI HQ Call no: e-copy only Record No: H052083)
https://www.frontiersin.org/articles/10.3389/fsufs.2023.1084728/pdf
https://vlibrary.iwmi.org/pdf/H052083.pdf
https://vlibrary.iwmi.org/pdf/H052083.pdf
(6.59 MB) (6.59 MB)
Introduction: The increased frequency of extreme climate events, many of them of rapid onset, observed in many world regions, demands the development of a crop forecasting system for hazard preparedness based on both intraseasonal and extended climate prediction. This paper presents a Fortran version of the Crop Productivity Model AquaCrop that assesses climate and soil fertility effects on yield gap, which is crucial in crop forecasting systems
Methods: Firstly, the Fortran version model - AQF outputs were compared to the latest version of AquaCrop v 6.1. The computational performance of both versions was then compared using a 100-year hypothetical experiment. Then, field experiments combining fertility and water stress on productivity were used to assess AQF model simulation. Finally, we demonstrated the applicability of this software in a crop operational forecast system.
Results: Results revealed that the Fortran version showed statistically similar results to the original version (r 2 > 0.93 and RMSEn < 11%, except in one experiment) and better computational efficiency. Field data indicated that AQF simulations are in close agreement with observation.
Conclusions: AQF offers a version of the AquaCrop developed for time-consuming applications, such as crop forecast systems and climate change simulations over large areas and explores mitigation and adaptation actions in the face of adverse effects of future climate change.
Methods: Firstly, the Fortran version model - AQF outputs were compared to the latest version of AquaCrop v 6.1. The computational performance of both versions was then compared using a 100-year hypothetical experiment. Then, field experiments combining fertility and water stress on productivity were used to assess AQF model simulation. Finally, we demonstrated the applicability of this software in a crop operational forecast system.
Results: Results revealed that the Fortran version showed statistically similar results to the original version (r 2 > 0.93 and RMSEn < 11%, except in one experiment) and better computational efficiency. Field data indicated that AQF simulations are in close agreement with observation.
Conclusions: AQF offers a version of the AquaCrop developed for time-consuming applications, such as crop forecast systems and climate change simulations over large areas and explores mitigation and adaptation actions in the face of adverse effects of future climate change.
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