scholarly journals Possible Scenarios of Winter Wheat Yield Reduction of Dryland Qazvin Province, Iran, Based on Prediction of Temperature and Precipitation Till the End of the Century

Climate ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 78 ◽  
Author(s):  
Behnam Mirgol ◽  
Meisam Nazari
Keyword(s):  
Winter Wheat ◽  
Crop Yields ◽  
Wheat Yield ◽  
Annual Precipitation ◽  
Yield Reduction ◽  
Factors Affecting ◽  
Temperature And Precipitation ◽  
Mean Temperature ◽  
Winter Wheat Yield ◽  
Semi Arid

The climate of the Earth is changing. The Earth’s temperature is projected to maintain its upward trend in the next few decades. Temperature and precipitation are two very important factors affecting crop yields, especially in arid and semi-arid regions. There is a need for future climate predictions to protect vulnerable sectors like agriculture in drylands. In this study, the downscaling of two important climatic variables—temperature and precipitation—was done by the CanESM2 and HadCM3 models under five different scenarios for the semi-arid province of Qazvin, located in Iran. The most efficient scenario was selected to predict the dryland winter wheat yield of the province for the three periods: 2010–2039, 2040–2069, and 2070–2099. The results showed that the models are able to satisfactorily predict the daily mean temperature and annual precipitation for the three mentioned periods. Generally, the daily mean temperature and annual precipitation tended to decrease in these periods when compared to the current reference values. However, the scenarios rcp2.6 and B2, respectively, predicted that the precipitation will fall less or even increase in the period 2070–2099. The scenario rcp2.6 seemed to be the most efficient to predict the dryland winter wheat yield of the province for the next few decades. The grain yield is projected to drop considerably over the three periods, especially in the last period, mainly due to the reduction in precipitation in March. This leads us to devise some adaptive strategies to prevent the detrimental impacts of climate change on the dryland winter wheat yield of the province.

Winter Wheat Yield Reduction from Interference by Italian Ryegrass 1

1976 ◽  
Vol 68 (3) ◽  
pp. 463-466 ◽  
Author(s):  
A. P. Appleby ◽  
P. D. Olson ◽  
D. R. Colbert
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Italian Ryegrass ◽  
Yield Reduction ◽  
Winter Wheat Yield

Leaf Rust Resistance Gene Lr9 and Winter Wheat Yield Reduction: I. Yield and Yield Components

Crop Science ◽  
1996 ◽  
Vol 36 (6) ◽  
pp. 1590-1595 ◽  
Author(s):  
Silvano Ortelli ◽  
Hans Winzeler ◽  
Michael Winzeler ◽  
Padruot M. Fried ◽  
Josef Nösberger
Keyword(s):  
Winter Wheat ◽  
Leaf Rust ◽  
Yield Components ◽  
Rust Resistance ◽  
Wheat Yield ◽  
Leaf Rust Resistance ◽  
Yield Reduction ◽  
Leaf Rust Resistance Gene ◽  
Yield And Yield Components ◽  
Winter Wheat Yield

scholarly journals Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study

2018 ◽  
Vol 10 (10) ◽  
pp. 1659 ◽  
Author(s):  
Inbal Becker-Reshef ◽  
Belen Franch ◽  
Brian Barker ◽  
Emilie Murphy ◽  
Andres Santamaria-Artigas ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Time Series Data ◽  
Crop Yields ◽  
Wheat Yield ◽  
Series Data ◽  
Crop Type ◽  
Winter Wheat Yield

Monitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, as it enables the extraction of crop-specific signals. Yet, the availability of such data within the growing season is often limited. Within this context, this study seeks to develop a practical approach to extract a crop-specific signal for yield forecasting in cases where crop rotations are prevalent, and detailed in-season information on crop type distribution is not available. We investigated the possibility of accurately forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data. The main idea is to explore an optimal spatial resolution at which crop type changes will be negligible due to crop rotation (so a previous seasons’ mask, which is more readily available can be used) and an informative signal can be extracted, so it can be correlated to crop yields. The study was carried out in the United States of America (USA) and utilized multiple years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) detailed wheat masks, and a regression-based winter wheat yield model. The results indicate that, in places where crop rotations were prevalent, coarsening the spatial scale of a crop type mask from the previous season resulted in a constant per-pixel wheat proportion over multiple seasons. This enables the consistent extraction of a crop-specific vegetation index time series that can be used for in-season monitoring and yield estimation over multiple years using a single mask. In the case of the USA, using a moderate resolution crop type mask from a previous season aggregated to 5 km resolution, resulted in a 0.7% tradeoff in accuracy relative to the control case where annually-updated detailed crop-type masks were available. These findings suggest that when detailed in-season data is not available, winter wheat yield can be accurately forecasted (within 10%) prior to harvest using a single, prior season crop mask and coarse resolution Normalized Difference Vegetation Index (NDVI) time series data.

scholarly journals The effect of soil and weather conditions on yields of winter wheat in multi-environmental trials

2019 ◽  
Vol 56 (2) ◽  
pp. 263-279 ◽  
Author(s):  
Marzena Iwańska ◽  
Michał Stępień
Keyword(s):  
Winter Wheat ◽  
Crop Yields ◽  
Wheat Yield ◽  
Weather Conditions ◽  
Grain Filling ◽  
Growth Stages ◽  
Yield Data ◽  
Winter Wheat Yield ◽  
Crop Growth Stage ◽  
The Impact

SummaryDrought reduces crop yields not only in areas of arid climate. The impact of droughts depends on the crop growth stage and soil properties. The frequency of droughts will increase due to climate change. It is important to determine the environmental variables that have the strongest effect on wheat yields in dry years. The effect of soil and weather on wheat yield was evaluated in 2018, which was considered a very dry year in Europe. The winter wheat yield data from 19 trial locations of the Research Center of Cultivar Testing (COBORU), Poland, were used. Soil data from the trial locations, mean air temperature (T) and precipitation (P) were considered as environmental factors, as well as the climatic water balance (CWB). The hydrothermal coefficient (HTC), which is based on P and T, was also used. The effect of these factors on winter wheat yield was related to the weather conditions at particular growth stages. The soil had a greater effect than the weather conditions. CWB, P, T and HTC showed a clear relationship with winter wheat yield. Soil data and HTC are the factors most recommended for models predicting crop yields. In the selection of drought-tolerant genotypes, the plants should be subjected to stress especially during the heading and grain filling growth stages.

scholarly journals Spatial Heterogeneity of Winter Wheat Yield and Its Determinants in the Yellow River Delta, China

2019 ◽  
Vol 12 (1) ◽  
pp. 135
Author(s):  
Lin Chu ◽  
Chong Huang ◽  
Qingsheng Liu ◽  
Chongfa Cai ◽  
Gaohuan Liu
Keyword(s):  
Winter Wheat ◽  
Soil Salinity ◽  
Crop Yields ◽  
Yellow River ◽  
Wheat Yield ◽  
Yellow River Delta ◽  
The Yellow River ◽  
The Yellow River Delta ◽  
Winter Wheat Yield ◽  
Actual Yield

Understanding spatial differences of crop yields and quantitatively exploring the relationship between crop yields and influencing factors are of great significance in increasing regional crop yields, promoting sustainable development of regional agriculture and ensuring regional food security. This study investigates spatial heterogeneity of winter wheat yield and its determinants in the Yellow River Delta (YRD) region. The spatial pattern of winter wheat in 2015 was mapped through time series similarity analysis. Winter wheat yield was estimated by integrating phenological information into yield model, and cross-validation was performed using actual yield data. The geographical detector method was used to analyze determinants influencing winter wheat yield. This study concluded that the overall classification accuracy for winter wheat is 88.09%. The estimated yield agreed with actual yield, with R2 value of 0.74 and root mean square error (RMSE) of 1.02 t ha−1. Cumulative temperature, soil salinity and their interactions were key determinants affecting winter wheat yield. Several measures are recommended to ensure sustainable crop production in the YRD region, including improving irrigation and drainage systems to reduce soil salinity, selecting salt-tolerant winter wheat varieties, and improving agronomy techniques to extend effective cumulative temperature.

scholarly journals Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches

2020 ◽  
Vol 12 (11) ◽  
pp. 1744 ◽  
Author(s):  
Xinlei Wang ◽  
Jianxi Huang ◽  
Quanlong Feng ◽  
Dongqin Yin
Keyword(s):  
Deep Learning ◽  
Winter Wheat ◽  
Uncertainty Analysis ◽  
Agricultural Sector ◽  
Crop Yields ◽  
Regional Scale ◽  
Wheat Yield ◽  
Yield Prediction ◽  
County Level ◽  
Winter Wheat Yield

Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector. However, winter wheat yield estimation and forecasting on a regional scale still remains challenging. In this study, we established a two-branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level. The first branch of the model was constructed based on the Long Short-Term Memory (LSTM) networks with inputs from meteorological and remote sensing data. Another branch was constructed using Convolution Neural Networks (CNN) to model static soil features. The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave-one-year-out-validation. The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg/ha, respectively. We further conducted yield prediction and uncertainty analysis based on the two-branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg/ha. Results also showed that while yield detrending could potentially introduce higher uncertainty, it had the advantage of improving the model performance in yield prediction.

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