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
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
<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
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.
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.
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.
