Competition for solar radiation between plants grown in multi-species cropping systems can severely limit crop production of individual species within that system. There are various approaches for modeling light inter...Competition for solar radiation between plants grown in multi-species cropping systems can severely limit crop production of individual species within that system. There are various approaches for modeling light interception within mixed-cropping and row or strip intercropping systems. To extend the knowledge about model behavior and different model approaches under interspecific competition conditions, the Agricultural Production Systems Simulator （APSIM） was evaluated and calibrated for field experiments previously described and simulated by the Decision Support System for Agrotechnology Transfer （DSSAT）. Initially the APSIM plant model was successfully modified to simulate wheat, maize and fieldpea monocultures in the European agro-ecological zone. Once calibrated, the APSIM model was then used to simulate a strip relay intercropping maize/wheat and maize/fieldpea system. In DSSAT, a shading algorithm was introduced to modify the daily weather input in order to take competition for solar radiation into account. In contrast, APSIM simulates interspecific competition using a modified Beer＇s law for multi-component canopy conditions. After a re-evaluation of the model regarding a minimum change of crop coefficients and variables, APSIM was able to simulate dry matter and grain yield of German maize, wheat and fieldpea varieties adequately. However, APSIM is a point-based model, and many of the processes that influence strip cropping cannot be accommodated by adjusting Beer＇s Law alone. So far none of the tested frameworks successfully modeled strip or relay intercropping. The processes governing growth in the numerous and very diversifying intercropping systems are complex and at this point in time have not been captured in sufficient detail.展开更多
[Objective] The paper was to study the effects of different ratios of N, P and K on yield of potato intercropped with sugarcane in Lateritic red earth area of Guangxi, and seek the best N, P and K ratio for nutrition ...[Objective] The paper was to study the effects of different ratios of N, P and K on yield of potato intercropped with sugarcane in Lateritic red earth area of Guangxi, and seek the best N, P and K ratio for nutrition model of potato inter- cropped with sugarcane. [Method]Two field experiments adopted the optimum com- pound design （311-A） were conducted in Long＇an County of Guangxi Province in 2011 and 2012, respectively. The polynomial regression models of fertilizer applica- tion and quadratic of three factors were established by SAS statistical analysis soft- ware, and optimum nutrient simulation models of potato were obtained by computer processing. [Result] The combined application of low nitrogen and mid-high potassi- um and phosphorus fertilizer contributed to higher potato yield in experimental condi- tion. The regression model of potato yield （Yll and Y12） and dosage of N（X1）, P （X2）, K（X3） were established by using SAS statistical analysis software, in 2011 and 2012, respectively. They were Y11 =14 725.28 -415.39X1 ＋741.99X2 ＋607.83）（3-447.92X1X2- 144.09X1X3 -405.83X2X3 -267.82X1^2-795.67X2^2 -642.10X3^2, R =0.927 2; and Y12 =14 342.60 -896.25X1 ＋548.62X2 ＋925.51 X3 ＋67.81 X1X2 ＋531.60X1X3 -99.00X2X3 -904.00X1^2 - 1121.36X2^2-596.64X3^2,R=0.926 6. The regression mathematics model of potato yields preferably fit with actual situation in the locality, and have higher practical value, so it could be used for fertilizer decision and forecast. Using the computer to carry on the optimization, the N, P and K dosage of the best potato yield intercropped with sugarcane was obtained. The dosage of N, P2O5, K2O were 108.8-140.6, 172.5-204.4 and 285.9 kg/hm2, respectively. [Conclusion] The best N, P and K ratio of potato yield intercropped with sugarcane was 1：（1.23-1.68）：（2.03-2.63）.展开更多
Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or compu...Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.展开更多
采用微型蒸渗仪(M icro-lys im eter)观测了单作和间作两种不同种植模式下冬小麦棵间土壤蒸发,分析了两种不同种植模式下棵间土壤蒸发的变化规律,探讨了影响麦田棵间土壤蒸发的主要因素,在此基础上对单作和间作麦田采用多元回归分析,建...采用微型蒸渗仪(M icro-lys im eter)观测了单作和间作两种不同种植模式下冬小麦棵间土壤蒸发,分析了两种不同种植模式下棵间土壤蒸发的变化规律,探讨了影响麦田棵间土壤蒸发的主要因素,在此基础上对单作和间作麦田采用多元回归分析,建立了两种种植模式下估算棵间土壤蒸发的数学模型,所建模型模拟精度较高,模拟值与实测值吻合较好。展开更多
文摘Competition for solar radiation between plants grown in multi-species cropping systems can severely limit crop production of individual species within that system. There are various approaches for modeling light interception within mixed-cropping and row or strip intercropping systems. To extend the knowledge about model behavior and different model approaches under interspecific competition conditions, the Agricultural Production Systems Simulator （APSIM） was evaluated and calibrated for field experiments previously described and simulated by the Decision Support System for Agrotechnology Transfer （DSSAT）. Initially the APSIM plant model was successfully modified to simulate wheat, maize and fieldpea monocultures in the European agro-ecological zone. Once calibrated, the APSIM model was then used to simulate a strip relay intercropping maize/wheat and maize/fieldpea system. In DSSAT, a shading algorithm was introduced to modify the daily weather input in order to take competition for solar radiation into account. In contrast, APSIM simulates interspecific competition using a modified Beer＇s law for multi-component canopy conditions. After a re-evaluation of the model regarding a minimum change of crop coefficients and variables, APSIM was able to simulate dry matter and grain yield of German maize, wheat and fieldpea varieties adequately. However, APSIM is a point-based model, and many of the processes that influence strip cropping cannot be accommodated by adjusting Beer＇s Law alone. So far none of the tested frameworks successfully modeled strip or relay intercropping. The processes governing growth in the numerous and very diversifying intercropping systems are complex and at this point in time have not been captured in sufficient detail.
基金Supported by Guangxi Science and Technology Research Projects (GKG10100004-10)The Earmarked Fund for China Agriculture Research System (CARS-20-3-5)Science and Technology Development Fund Project of Guangxi Academy of Agricultural Science (GNK 2011jz07)~~
文摘[Objective] The paper was to study the effects of different ratios of N, P and K on yield of potato intercropped with sugarcane in Lateritic red earth area of Guangxi, and seek the best N, P and K ratio for nutrition model of potato inter- cropped with sugarcane. [Method]Two field experiments adopted the optimum com- pound design （311-A） were conducted in Long＇an County of Guangxi Province in 2011 and 2012, respectively. The polynomial regression models of fertilizer applica- tion and quadratic of three factors were established by SAS statistical analysis soft- ware, and optimum nutrient simulation models of potato were obtained by computer processing. [Result] The combined application of low nitrogen and mid-high potassi- um and phosphorus fertilizer contributed to higher potato yield in experimental condi- tion. The regression model of potato yield （Yll and Y12） and dosage of N（X1）, P （X2）, K（X3） were established by using SAS statistical analysis software, in 2011 and 2012, respectively. They were Y11 =14 725.28 -415.39X1 ＋741.99X2 ＋607.83）（3-447.92X1X2- 144.09X1X3 -405.83X2X3 -267.82X1^2-795.67X2^2 -642.10X3^2, R =0.927 2; and Y12 =14 342.60 -896.25X1 ＋548.62X2 ＋925.51 X3 ＋67.81 X1X2 ＋531.60X1X3 -99.00X2X3 -904.00X1^2 - 1121.36X2^2-596.64X3^2,R=0.926 6. The regression mathematics model of potato yields preferably fit with actual situation in the locality, and have higher practical value, so it could be used for fertilizer decision and forecast. Using the computer to carry on the optimization, the N, P and K dosage of the best potato yield intercropped with sugarcane was obtained. The dosage of N, P2O5, K2O were 108.8-140.6, 172.5-204.4 and 285.9 kg/hm2, respectively. [Conclusion] The best N, P and K ratio of potato yield intercropped with sugarcane was 1：（1.23-1.68）：（2.03-2.63）.
文摘Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.
文摘采用微型蒸渗仪(M icro-lys im eter)观测了单作和间作两种不同种植模式下冬小麦棵间土壤蒸发,分析了两种不同种植模式下棵间土壤蒸发的变化规律,探讨了影响麦田棵间土壤蒸发的主要因素,在此基础上对单作和间作麦田采用多元回归分析,建立了两种种植模式下估算棵间土壤蒸发的数学模型,所建模型模拟精度较高,模拟值与实测值吻合较好。
