Simulated Growing‐Season Precipitation and Nitrogen Effects on Winter Wheat Yield

1991 ◽  
Vol 83 (1) ◽  
pp. 180-185 ◽  
Author(s):  
R. E. Engel
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Growing Season ◽  
Winter Wheat Yield ◽  
Growing Season Precipitation

scholarly journals An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1088 ◽  
Author(s):  
Yulong Wang ◽  
Xingang Xu ◽  
Linsheng Huang ◽  
Guijun Yang ◽  
Lingling Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
Crop Yield ◽  
Satellite Remote Sensing ◽  
Wheat Yield ◽  
Growing Season ◽  
Remote Sensing Images ◽  
Casa Model ◽  
Winter Wheat Yield

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

scholarly journals Climate Change Impacts on Winter Wheat Yield in Northern China

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiu Geng ◽  
Fang Wang ◽  
Wei Ren ◽  
Zhixin Hao
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Negative Impact ◽  
Climatic Factors ◽  
Wheat Yield ◽  
Growing Season ◽  
Northern China ◽  
Series Data ◽  
Winter Wheat Yield ◽  
Impacts Of Climate Change

Exploring the impacts of climate change on agriculture is one of important topics with respect to climate change. We quantitatively examined the impacts of climate change on winter wheat yield in Northern China using the Cobb–Douglas production function. Utilizing time-series data of agricultural production and meteorological observations from 1981 to 2016, the impacts of climatic factors on wheat production were assessed. It was found that the contribution of climatic factors to winter wheat yield per unit area (WYPA) was 0.762–1.921% in absolute terms. Growing season average temperature (GSAT) had a negative impact on WYPA for the period of 1981–2016. A 1% increase in GSAT could lead to a loss of 0.109% of WYPA when the other factors were constant. While growing season precipitation (GSP) had a positive impact on WYPA, as a 1% increase in GSP could result in 0.186% increase in WYPA, other factors kept constant. Then, the impacts on WYPA for the period 2021–2050 under two different emissions scenarios RCP4.5 and RCP8.5 were forecasted. For the whole study area, GSAT is projected to increase 1.37°C under RCP4.5 and 1.54°C under RCP8.5 for the period 2021–2050, which will lower the average WYPA by 1.75% and 1.97%, respectively. GSP is tended to increase by 17.31% under RCP4.5 and 22.22% under RCP8.5 and will give a rise of 3.22% and 4.13% in WYPA. The comprehensive effect of GSAT and GSP will increase WYPA by 1.47% under RCP4.5 and 2.16% under RCP8.5.

scholarly journals Diurnal Temperature Range in Winter Wheat Growing Regions of China: CMIP6 Model Evaluation and Comparison

2021 ◽  
Author(s):  
Wenqiang Xie ◽  
Shuangshuang Wang ◽  
Xiaodong Yan
Keyword(s):  
Winter Wheat ◽  
Diurnal Temperature Range ◽  
Climate Model ◽  
Wheat Yield ◽  
Coupled Model ◽  
Growing Season ◽  
Skill Score ◽  
Diurnal Temperature ◽  
Temperature Range ◽  
Winter Wheat Yield

Abstract Diurnal temperature range (DTR) is an important meteorological component affecting the yield and protein content of winter wheat. The accuracy of climate model simulations of DTR will directly affect the prediction of winter wheat yield and quality. Previous model evaluations for worldwide or nationwide cannot answer which model is suitable for the estimation of winter wheat yield. We evaluated the ability of the coupled model intercomparison project phase 6 (CMIP6) models to simulate DTR in the winter wheat growing regions of China using CN05 observations. The root mean square error (RMSE) and the interannual varibility skill score (IVS) were used to quantitatively evaluate the ability of models in simulating DTR spatial and temporal characteristics, and the comprehensive rating index (CRI) was used to determine the most suitable climate model for winter wheat. The results showed that the CMIP6 model can reproduce DTR in winter wheat growing regions. BCC-CSM2-MR simulations of DTR in the winter wheat growing season were more consistent with observations. EC-Earth3-Veg simulated the climatological DTR best in the wheat growing regions (RMSE=0.848). Meanwhile, the evaluation for climatological DTR in China is not applicable to the evaluation of DTR in winter wheat growing regions, and the evaluation for annual DTR is not a substitute for the evaluation for winter wheat growing season DTR. Our study highlights the importance of evaluating winter wheat growing regions' DTR, which can further improve the ability of CMIP6 models simulating DTR to serve the research of climate change impact on winter wheat yield.

scholarly journals Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

2020 ◽  
Vol 12 (8) ◽  
pp. 1232 ◽  
Author(s):  
Yumiao Wang ◽  
Zhou Zhang ◽  
Luwei Feng ◽  
Qingyun Du ◽  
Troy Runge
Keyword(s):  
United States ◽  
Machine Learning ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Growing Season ◽  
The United States ◽  
Learning Approaches ◽  
Source Data ◽  
The World ◽  
Winter Wheat Yield

Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined.

Effect of nitrogen top-dressing on winter wheat yield, quantity and quality

2005 ◽  
Vol 34 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Zs. Szentpétery ◽  
Cs. Kleinheincz ◽  
G. Szöllősi ◽  
M. Jolánkai
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Winter Wheat Yield

Smallseed Falseflax (Camelina microcarpa) Management in Oklahoma Winter Wheat

2021 ◽  
pp. 1-14
Author(s):  
Jodie A. Crose ◽  
Misha R. Manuchehri ◽  
Todd A. Baughman
Keyword(s):  
Winter Wheat ◽  
Weed Control ◽  
Herbicide Resistance ◽  
Wheat Yield ◽  
Acetolactate Synthase ◽  
Growing Season ◽  
Time Of Application ◽  
Adequate Control ◽  
Growing Seasons

Abstract Three herbicide premixes have recently been introduced for weed control in wheat. These include: halauxifen + florasulam, thifensulfuron + fluroxypyr, and bromoxynil + bicyclopyrone. The objective of this study was to evaluate these herbicides along with older products for their control of smallseed falseflax in winter wheat in Oklahoma. Studies took place during the 2017, 2018, and 2020 winter wheat growing seasons. Weed control was visually estimated every two weeks throughout the growing season and wheat yield was collected in all three years. Smallseed falseflax size was approximately six cm in diameter at time of application in all years. Control ranged from 96 to 99% following all treatments with the exception of bicyclopyrone + bromoxynil and dicamba alone, which controlled falseflax 90%. All treatments containing an acetolactate synthase (ALS)-inhibiting herbicide achieved adequate control; therefore, resistance is not suspected in this population. Halauxifen + florasulam and thifensulfuron + fluroxypyr effectively controlled smallseed falseflax similarly to other standards recommended for broadleaf weed control in wheat in Oklahoma. Rotational use of these products allows producers flexibility in controlling smallseed falseflax and reduces the potential for development of herbicide resistance in this species.

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