Sensitivity analysis （SA） is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application. The objectives were to （...Sensitivity analysis （SA） is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application. The objectives were to （i） determine influential and non-influential parameters with respect to above ground biomass （AGB）, canopy cover （CC）, and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments （rainfall, normal irrigation, and over-irrigation）; and （ii） generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results. In this study, field experiments were conducted during the 2012-2013,2013-2014, and 2014-2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China. The extended Fourier amplitude sensitivity test （EFAST） method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013-2014 growing season were used to calibrate the AquaCrop model, and data from 2012-2013 and 2014-2015 growing seasons were val- idated. For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis. For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions （normal irrigation and over-irrigation）, there were more sensitive parameters under water stress （rainfall）, while root development parameters were more sensitive. For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress. Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters （growing degree-days （GDD） from sowing to the maximum rooting depth （root） and the maximum effective rooting depth （rtx）） under water stress than under no water stress. The results showed that there was higher accuracy under water stress than under no water stress. This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.展开更多
Temperature compensatory effect, which quantifies the increase in cumulative air temperature from soil temperature increase caused by mulching, provides an effective method for incorporating soil temperature into crop...Temperature compensatory effect, which quantifies the increase in cumulative air temperature from soil temperature increase caused by mulching, provides an effective method for incorporating soil temperature into crop models. In this study, compensated temperature was integrated into the AquaCrop model to investigate the capability of the compensatory effect to improve assessment of the promotion of maize growth and development by plastic film mulching(PM). A three-year experiment was conducted from2014 to 2016 with two maize varieties(spring and summer) and two mulching conditions(PM and non-mulching(NM)), and the AquaCrop model was employed to reproduce crop growth and yield responses to changes in NM, PM, and compensated PM. A marked difference in soil temperature between NM and PM was observed before 50 days after sowing(DAS) during three growing seasons. During sowing–emergence and emergence–tasseling, the increase in air temperature was proportional to the compensatory coefficient, with spring maize showing a higher compensatory temperature than summer maize. Simulation results for canopy cover(CC) were generally in good agreement with the measurements, whereas predictions of aboveground biomass and grain yield under PM indicated large underestimates from 60 DAS to the end of maturity. Simulations of spring maize biomass and yield showed general increase based on temperature compensation, accompanied by improvement in modeling accuracy, with RMSEs decreasing from 2.5 to 1.6 t ha^(-1)and from 4.1 t to 3.4 t ha^(-1). Improvement in biomass and yield simulation was less pronounced for summer than for spring maize, implying that crops grown during low-temperature periods would benefit more from the compensatory effect. This study demonstrated the effectiveness of the temperature compensatory effect to improve the performance of the AquaCrop model in simulating maize growth under PM practices.展开更多
<span style="font-family:Verdana;">Modeling of irrigation methods </span><span style="font-family:Verdana;">is</span><span style="font-family:""><spa...<span style="font-family:Verdana;">Modeling of irrigation methods </span><span style="font-family:Verdana;">is</span><span style="font-family:""><span style="font-family:Verdana;"> one of the most important techniques that contribute to the future of modern agriculture. This will conserve water as water scarcity is a major threat for agriculture. In this study, AquaCrop model was used to model different irrigation methods of maize in field trails in Al-Yousifya, 15 km Southwest of Baghdad. Field experiments were conducted for two seasons during 2016 and 2017 using five irrigation methods including furrow, surface drip and subsurface drip with three patterns of emitter depth (10, 20 and 30 cm) irrigation. AquaCrop simulations of biomass, grain yield, harvest index and water productivity were validated using different statistical parameters under the natural conditions obtained in the study area. For 2016 and 2017 seasons, results of R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> were 0.98 and 0.99, 0.99 and 0.99, 0.99 and 0.97, and 0.8 and 0.73 for biomass, grain yield, harvest index and water productivity, respectively. The study has conducted that simulation using AquaCrop is considered very efficient tool for modeling of different irrigation applications</span><span style="font-family:Verdana;"> for maize production under the existing conditions</span><span style="font-family:Verdana;"> in the central region of Iraq.展开更多
AquaCrop model estimates the crop productivity decrease in response to water stress, determining the biomass (B) based on water productivity (WP) and accumulated transpiration (ΣTr);and the yield (Y) is calculated ac...AquaCrop model estimates the crop productivity decrease in response to water stress, determining the biomass (B) based on water productivity (WP) and accumulated transpiration (ΣTr);and the yield (Y) is calculated according to B and the harvest index (HI). AquaCrop was evaluated considering different WP values for 2010 late growing season to simulate crop yield of potato (Solanum tuberosum L.) cv. Spunta, in a commercial production field of 9 ha located in the green belt of Cordoba city (31°30'S, 64°08'W, 402 m asl), while monitoring in 2009 was used to verify the model. Canopy cover estimation by AquaCrop was adjusted using observed field data obtained from vertical digital photographs acquired at 2.5 m height. WP values of 15.8 and 31.6 (for C3 and C4 species, respectively) and two intermediate values 21 and 26.3 g·mˉ2 were considered to evaluate the model performance. While linear function between observed tuber yields and estimated by AquaCrop had always a correlation coefficient greater than 0.94 (p 0.001), using WP = 26.3 and WP =31.6 g·mˉ2 presented overestimation, whereas with 15.8 g·mˉ2 had an opposite behavior, while WP = 21 g·mˉ2 was the value that produced the lowest estimation error. In addition, soil moisture from this estimated value of WP was highly correlated with measured water content in different areas of production field. The verification test shows that while the model slightly underestimates canopy cover, biomass was overestimated. After setting the coefficients of canopy cover development, the AquaCrop crop model estimated adequately potato yield for high production values that are less affected by lack of water, but in both years showed a tendency to overestimate the lowest yields, as was observed for other crops. Meanwhile, the dispersion between the observed and estimated yield was higher in the verification test because the sampling this year was more random.展开更多
This study aimed to enhance the utilization of agricultural waste and identify the most suitable agricultural waste materials for tomato cultivation. It utilized a locally modified substrate labeled as CK, along with ...This study aimed to enhance the utilization of agricultural waste and identify the most suitable agricultural waste materials for tomato cultivation. It utilized a locally modified substrate labeled as CK, along with five different groups of agricultural waste materials, designated as T1 (organic fertilizer: loessial soil: straw in a ratio of 4:5:1), T2 (organic fertilizer: loessial soil: straw: grains in a ratio of 3:5:1:1), T3 (organic fertilizer: loessial soil: straw: grains in a ratio of 2:5:1:2), T4 (organic fertilizer:loessial soil:straw:grains in a ratio of 1:5:1:3), and T5 ( loessial soil:straw:grains in a ratio of 5:1:4), the AquaCrop model was employed to validate soil water content and tomato growth and yield under these treatments. Furthermore, a multi-objective genetic algorithm was employed to determine the optimal agricultural waste materials that would ensure maximum tomato yield, water use efficiency (WUE), partial factor productivity of fertilizer (PFP) and sugar-acid ratio. The results indicated that the AquaCrop model reasonably simulated volumetric soil water content, tomato canopy cover, and biomass, with root mean square error (RMSE) ranges of 20.0-69.4 mm, 15.2%-25.1%, and 1.093-3.469 t/hm2, respectively. The CK group exhibited an R-squared (R2) value of 0.63 for volumetric soil water contents, while the ratio scenarios showed R2 values exceeding 0.80. The multi-objective genetic optimization algorithm identified T5 as the optimal ratio scenario, resulting in maximum tomato yield, WUE, PFP, and quality. This study offers a theoretical foundation for the efficient utilization of agricultural wastes and the production of high-quality fruits and vegetables.展开更多
为探究南疆核桃适宜的地下滴灌灌溉制度,该研究于2023年和2024年在新疆阿克苏地区对树龄16 a的“温185”核桃进行试验,设置75%ET_(c)(ET_(c)为作物蒸发蒸腾量,W1)、100%ET_(c)(W2)、125%ET_(c)(W3)和150%ET_(c)(W4)4种灌水定额处理,构...为探究南疆核桃适宜的地下滴灌灌溉制度,该研究于2023年和2024年在新疆阿克苏地区对树龄16 a的“温185”核桃进行试验,设置75%ET_(c)(ET_(c)为作物蒸发蒸腾量,W1)、100%ET_(c)(W2)、125%ET_(c)(W3)和150%ET_(c)(W4)4种灌水定额处理,构建了基于AquaCrop模型和NSGA-II算法的核桃灌溉制度优化模型,以产量最大化,灌水量最小为目标函数对模型求解,通过利用优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS)对最优解集进行评价,得到南疆地下滴灌核桃的最优灌溉制度。结果表明,适当增加灌水定额可以提高核桃产量,水分利用效率与灌溉水利用效率随灌水定额的增加而减少;AquaCrop模型模拟2 a冠层覆盖度的决定系数R2≥0.94,均方根误差(root mean square error,RMSE)和标准化均方根误差(normal1zed root mean square error,NRMSE)分别为3.01%~8.52%和5.02%~17.71%,Nash效率系数(Nash-Sutcliffe model effciency coefficient,EF)和拟合度指数(d)分别为0.57~0.89和0.90~0.99;模拟值与实测土壤含水率R2≥0.82,RMSE和NRMSE分别为9.02%~21.51%和4.31%~9.81%,EF和d分别为0.41~0.79和0.89~0.95;产量模拟值与实测值R2、RMSE、NRMSE、EF和d变化范围分别为0.89~0.95、114.57~178.73 kg/hm^(2)、0.04%~0.05%、0.14~0.48和0.86~0.88;AquaCrop模拟不同灌水场景模拟结果表明,方案T9灌溉定额为6500 m^(3)/hm^(2),灌水周期为7 d时产量最高达4080.34 kg/hm^(2);基于AquaCrop-NSGA-II算法的双目标优化模型优化结果显示,最佳灌水方案S7灌水周期为11 d,总灌溉量为3520.22 m^(3)/hm^(2),产量为4021.75 kg/hm^(2),与AquaCrop模型模拟得到的最大产量对应的灌水方案T9相比,产量仅下降了1.44%,但灌溉用水量减少了2979.78 m^(3)/hm^(2)。推荐南疆核桃地下滴灌灌溉周期为11 d,灌水次数为10次,灌溉定额为3520.22 m^(3)/hm^(2)。该研究构建的基于AquaCrop模型模拟及多目标优化算法可以用于优化南疆核桃地下滴灌灌溉制度。展开更多
基金supported by the National Natural Science Foundation of China(41571416)the Natural Science Foundation of Beijing,China(4152019)the Beijing Academy of Agricultural and Forestry Sciences Innovation Capacity Construction Specific Projects,China(KJCX20150409)
文摘Sensitivity analysis （SA） is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application. The objectives were to （i） determine influential and non-influential parameters with respect to above ground biomass （AGB）, canopy cover （CC）, and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments （rainfall, normal irrigation, and over-irrigation）; and （ii） generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results. In this study, field experiments were conducted during the 2012-2013,2013-2014, and 2014-2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China. The extended Fourier amplitude sensitivity test （EFAST） method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013-2014 growing season were used to calibrate the AquaCrop model, and data from 2012-2013 and 2014-2015 growing seasons were val- idated. For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis. For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions （normal irrigation and over-irrigation）, there were more sensitive parameters under water stress （rainfall）, while root development parameters were more sensitive. For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress. Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters （growing degree-days （GDD） from sowing to the maximum rooting depth （root） and the maximum effective rooting depth （rtx）） under water stress than under no water stress. The results showed that there was higher accuracy under water stress than under no water stress. This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.
基金supported by the National Natural Science Foundation of China (51909228 and 52209071)the “High-level Talents Support Program” of Yangzhou University+2 种基金“Chunhui Plan” Cooperative Scientific Research Project of Ministry of Education of China (HZKY20220115)the China Postdoctoral Science Foundation (2020M671623)the “Blue Project” of Yangzhou University。
文摘Temperature compensatory effect, which quantifies the increase in cumulative air temperature from soil temperature increase caused by mulching, provides an effective method for incorporating soil temperature into crop models. In this study, compensated temperature was integrated into the AquaCrop model to investigate the capability of the compensatory effect to improve assessment of the promotion of maize growth and development by plastic film mulching(PM). A three-year experiment was conducted from2014 to 2016 with two maize varieties(spring and summer) and two mulching conditions(PM and non-mulching(NM)), and the AquaCrop model was employed to reproduce crop growth and yield responses to changes in NM, PM, and compensated PM. A marked difference in soil temperature between NM and PM was observed before 50 days after sowing(DAS) during three growing seasons. During sowing–emergence and emergence–tasseling, the increase in air temperature was proportional to the compensatory coefficient, with spring maize showing a higher compensatory temperature than summer maize. Simulation results for canopy cover(CC) were generally in good agreement with the measurements, whereas predictions of aboveground biomass and grain yield under PM indicated large underestimates from 60 DAS to the end of maturity. Simulations of spring maize biomass and yield showed general increase based on temperature compensation, accompanied by improvement in modeling accuracy, with RMSEs decreasing from 2.5 to 1.6 t ha^(-1)and from 4.1 t to 3.4 t ha^(-1). Improvement in biomass and yield simulation was less pronounced for summer than for spring maize, implying that crops grown during low-temperature periods would benefit more from the compensatory effect. This study demonstrated the effectiveness of the temperature compensatory effect to improve the performance of the AquaCrop model in simulating maize growth under PM practices.
文摘<span style="font-family:Verdana;">Modeling of irrigation methods </span><span style="font-family:Verdana;">is</span><span style="font-family:""><span style="font-family:Verdana;"> one of the most important techniques that contribute to the future of modern agriculture. This will conserve water as water scarcity is a major threat for agriculture. In this study, AquaCrop model was used to model different irrigation methods of maize in field trails in Al-Yousifya, 15 km Southwest of Baghdad. Field experiments were conducted for two seasons during 2016 and 2017 using five irrigation methods including furrow, surface drip and subsurface drip with three patterns of emitter depth (10, 20 and 30 cm) irrigation. AquaCrop simulations of biomass, grain yield, harvest index and water productivity were validated using different statistical parameters under the natural conditions obtained in the study area. For 2016 and 2017 seasons, results of R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> were 0.98 and 0.99, 0.99 and 0.99, 0.99 and 0.97, and 0.8 and 0.73 for biomass, grain yield, harvest index and water productivity, respectively. The study has conducted that simulation using AquaCrop is considered very efficient tool for modeling of different irrigation applications</span><span style="font-family:Verdana;"> for maize production under the existing conditions</span><span style="font-family:Verdana;"> in the central region of Iraq.
文摘AquaCrop model estimates the crop productivity decrease in response to water stress, determining the biomass (B) based on water productivity (WP) and accumulated transpiration (ΣTr);and the yield (Y) is calculated according to B and the harvest index (HI). AquaCrop was evaluated considering different WP values for 2010 late growing season to simulate crop yield of potato (Solanum tuberosum L.) cv. Spunta, in a commercial production field of 9 ha located in the green belt of Cordoba city (31°30'S, 64°08'W, 402 m asl), while monitoring in 2009 was used to verify the model. Canopy cover estimation by AquaCrop was adjusted using observed field data obtained from vertical digital photographs acquired at 2.5 m height. WP values of 15.8 and 31.6 (for C3 and C4 species, respectively) and two intermediate values 21 and 26.3 g·mˉ2 were considered to evaluate the model performance. While linear function between observed tuber yields and estimated by AquaCrop had always a correlation coefficient greater than 0.94 (p 0.001), using WP = 26.3 and WP =31.6 g·mˉ2 presented overestimation, whereas with 15.8 g·mˉ2 had an opposite behavior, while WP = 21 g·mˉ2 was the value that produced the lowest estimation error. In addition, soil moisture from this estimated value of WP was highly correlated with measured water content in different areas of production field. The verification test shows that while the model slightly underestimates canopy cover, biomass was overestimated. After setting the coefficients of canopy cover development, the AquaCrop crop model estimated adequately potato yield for high production values that are less affected by lack of water, but in both years showed a tendency to overestimate the lowest yields, as was observed for other crops. Meanwhile, the dispersion between the observed and estimated yield was higher in the verification test because the sampling this year was more random.
基金supported by the National Natural Science Foundation of China(Grant No.52379042)Key R&D plan of Gansu Province(Grant No.23YFFA0019)Gansu Province East-West Cooperation Project(Grant No.23CXNA0025).
文摘This study aimed to enhance the utilization of agricultural waste and identify the most suitable agricultural waste materials for tomato cultivation. It utilized a locally modified substrate labeled as CK, along with five different groups of agricultural waste materials, designated as T1 (organic fertilizer: loessial soil: straw in a ratio of 4:5:1), T2 (organic fertilizer: loessial soil: straw: grains in a ratio of 3:5:1:1), T3 (organic fertilizer: loessial soil: straw: grains in a ratio of 2:5:1:2), T4 (organic fertilizer:loessial soil:straw:grains in a ratio of 1:5:1:3), and T5 ( loessial soil:straw:grains in a ratio of 5:1:4), the AquaCrop model was employed to validate soil water content and tomato growth and yield under these treatments. Furthermore, a multi-objective genetic algorithm was employed to determine the optimal agricultural waste materials that would ensure maximum tomato yield, water use efficiency (WUE), partial factor productivity of fertilizer (PFP) and sugar-acid ratio. The results indicated that the AquaCrop model reasonably simulated volumetric soil water content, tomato canopy cover, and biomass, with root mean square error (RMSE) ranges of 20.0-69.4 mm, 15.2%-25.1%, and 1.093-3.469 t/hm2, respectively. The CK group exhibited an R-squared (R2) value of 0.63 for volumetric soil water contents, while the ratio scenarios showed R2 values exceeding 0.80. The multi-objective genetic optimization algorithm identified T5 as the optimal ratio scenario, resulting in maximum tomato yield, WUE, PFP, and quality. This study offers a theoretical foundation for the efficient utilization of agricultural wastes and the production of high-quality fruits and vegetables.
文摘为探究南疆核桃适宜的地下滴灌灌溉制度,该研究于2023年和2024年在新疆阿克苏地区对树龄16 a的“温185”核桃进行试验,设置75%ET_(c)(ET_(c)为作物蒸发蒸腾量,W1)、100%ET_(c)(W2)、125%ET_(c)(W3)和150%ET_(c)(W4)4种灌水定额处理,构建了基于AquaCrop模型和NSGA-II算法的核桃灌溉制度优化模型,以产量最大化,灌水量最小为目标函数对模型求解,通过利用优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS)对最优解集进行评价,得到南疆地下滴灌核桃的最优灌溉制度。结果表明,适当增加灌水定额可以提高核桃产量,水分利用效率与灌溉水利用效率随灌水定额的增加而减少;AquaCrop模型模拟2 a冠层覆盖度的决定系数R2≥0.94,均方根误差(root mean square error,RMSE)和标准化均方根误差(normal1zed root mean square error,NRMSE)分别为3.01%~8.52%和5.02%~17.71%,Nash效率系数(Nash-Sutcliffe model effciency coefficient,EF)和拟合度指数(d)分别为0.57~0.89和0.90~0.99;模拟值与实测土壤含水率R2≥0.82,RMSE和NRMSE分别为9.02%~21.51%和4.31%~9.81%,EF和d分别为0.41~0.79和0.89~0.95;产量模拟值与实测值R2、RMSE、NRMSE、EF和d变化范围分别为0.89~0.95、114.57~178.73 kg/hm^(2)、0.04%~0.05%、0.14~0.48和0.86~0.88;AquaCrop模拟不同灌水场景模拟结果表明,方案T9灌溉定额为6500 m^(3)/hm^(2),灌水周期为7 d时产量最高达4080.34 kg/hm^(2);基于AquaCrop-NSGA-II算法的双目标优化模型优化结果显示,最佳灌水方案S7灌水周期为11 d,总灌溉量为3520.22 m^(3)/hm^(2),产量为4021.75 kg/hm^(2),与AquaCrop模型模拟得到的最大产量对应的灌水方案T9相比,产量仅下降了1.44%,但灌溉用水量减少了2979.78 m^(3)/hm^(2)。推荐南疆核桃地下滴灌灌溉周期为11 d,灌水次数为10次,灌溉定额为3520.22 m^(3)/hm^(2)。该研究构建的基于AquaCrop模型模拟及多目标优化算法可以用于优化南疆核桃地下滴灌灌溉制度。
