Climate change impacts on winter wheat yield change – which climatic parameters are crucial in Pannonian lowland?

SUMMARYOne of the main problems in estimating the effects of climate change on crops is the identification of those factors limiting crop growth in a selected environment. Previous studies have indicated that considering simple trends of either precipitation or temperature for the coming decades is insufficient for estimating the climate impact on yield in the future. One reason for this insufficiency is that changes in weather extremes or seasonal weather patterns may have marked impacts.The present study focuses on identifying agroclimatic parameters that can identify the effects of climate change and variability on winter wheat yield change in the Pannonian lowland. The impacts of soil type under past and future climates as well as the effect of different CO2 concentrations on yield formation are also considered. The Vojvodina region was chosen for this case study because it is a representative part of the Pannonian lowland.Projections of the future climate were taken from the HadCM3, ECHAM5 and NCAR-PCM climate models with the SRES-A2 scenario for greenhouse gas (GHG) emissions for the 2040 and 2080 integration periods. To calibrate and validate the Met&Roll weather generator, four-variable weather data series (for six main climatic stations in the Vojvodina region) were analysed. The grain yield of winter wheat was calculated using the SIRIUS wheat model for three different CO2 concentrations (330, 550 and 1050 ppm) dependent on the integration period. To estimate the effects of climatic parameters on crop yield, the correlation coefficient between crop yield and agroclimatic indices was calculated using the AGRICLIM software. The present study shows that for all soil types, the following indices are the most important for winter wheat yields in this region: (i) the number of days with water and temperature stress, (ii) the accumulated precipitation, (iii) the actual evapotranspiration (ETa) and (iv) the water deficit during the growing season. The high positive correlations between yield and the ETa, accumulated precipitation and the ratio between the ETa and reference evapotranspiration (ETr) for the April–June period indicate that water is and will remain a major limiting factor for growing winter wheat in this region. Indices referring to negative impact on yield are (i) the number of days with a water deficit for the April–June period and (ii) the number of days with maximum temperature above 25 °C (summer days) and the number of days with maximum temperature above 30 °C (tropical days) in May and June. These indices can be seen as indicators of extreme weather events such as drought and heat waves.

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Excessive Rainfall Is the Key Meteorological Limiting Factor for Winter Wheat Yield in the Middle and Lower Reaches of the Yangtze River

Agronomy ◽

10.3390/agronomy12010050 ◽

2021 ◽

Vol 12 (1) ◽

pp. 50

Author(s):

Weiwei Liu ◽

Weiwei Sun ◽

Jingfeng Huang ◽

Huayang Wen ◽

Ran Huang

Keyword(s):

Climate Change ◽

Winter Wheat ◽

Yangtze River ◽

Wheat Yield ◽

Maximum Temperature ◽

Limiting Factor ◽

The Yangtze River ◽

Sunshine Hours ◽

Winter Wheat Yield ◽

The Impact

In the era of global climate change, extreme weather events frequently occur. Many kinds of agro-meteorological disasters that are closely related to environmental conditions (such as sunshine hours, temperature, precipitation, etc.) are witnessed all over the word. However, which factor dominates winter wheat production in the middle and lower reaches of the Yangtze River remains unresolved. Quantifying the key limiting meteorological factor could deepen our understanding of the impact of climate change on crops and then help us to formulate disaster prevention and mitigation measures. However, the relative role of precipitation, sunshine hours and maximum daily temperature in limiting winter wheat yield in the middle and lower reaches of the Yangtze River is not clear and difficult to decouple. In this study, we used statistical methods to quantify the effect of precipitation, maximum temperature and sunshine hours extremes on winter wheat (Triticum aestivum L.) yield based on long time-series, county-level yield data and a daily meteorological dataset. According to the winter wheat growing season period (October of the sowing year to May of the following year), anomaly values of cumulative precipitation, average sunshine hours and average daily maximum temperature are calculated. With the range of −3 σ to 3 σ of anomaly and an interval of 0.5 σ (σ is the corresponding standard deviation of cumulative precipitation, mean maximum temperature and mean sunshine hours, respectively), the corresponding weighted yield loss ratio (WYLR) represents the impact of this kind of climate condition on yield. The results show that excessive rainfall is the key limiting meteorological factor that can reduce winter wheat yield to −18.4% in the middle and lower reaches of the Yangtze River, while it is only −0.24% in extreme dry conditions. Moreover, yield loss under extreme temperature and sunshine hours are negligible (−0.66% for extremely long sunshine hours and −8.29% for extreme cold). More detailed analysis results show that the impact of excessive rainfall on winter wheat yield varies regionally, as it causes severe yield reductions in the Huai River basin and the middle to southern part with low elevation and rainy areas of the study area, while for drier areas in the Hubei province, there is even an increase in yield. Our results disclosed with observational evidence that excessive precipitation is the key meteorological limiting factor leading to the reduction in winter wheat yield in the middle and lower reaches of the Yangtze River. The knowledge of the possible impact of climate change on winter wheat yield in the study area allows policy-makers, agronomists and economists to better forecast a plan that differs from the past. In addition, our results emphasized the need for better understanding and further process-based model simulation of the excessive rainfall impact on crop yield.

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

Remote Sensing ◽

10.3390/rs11091088 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1088 ◽

Cited By ~ 4

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.

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Effects of climate change and adaptation options on winter wheat yield under rainfed Mediterranean conditions in southern Portugal

Climatic Change ◽

10.1007/s10584-019-02419-4 ◽

2019 ◽

Vol 154 (1-2) ◽

pp. 159-178 ◽

Cited By ~ 9

Author(s):

Chenyao Yang ◽

Helder Fraga ◽

Wim van Ieperen ◽

Henrique Trindade ◽

João A. Santos

Keyword(s):

Climate Change ◽

Winter Wheat ◽

Wheat Yield ◽

Winter Wheat Yield ◽

Southern Portugal ◽

Adaptation Options ◽

Mediterranean Conditions

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Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

10.21203/rs.3.rs-789462/v1 ◽

2021 ◽

Author(s):

Amit Kumar Srivast ◽

Nima Safaei ◽

Saeed Khaki ◽

Gina Lopez ◽

Wenzhi Zeng ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Winter Wheat ◽

Crop Yield ◽

Wheat Yield ◽

Yield Prediction ◽

Learning Models ◽

Winter Wheat Yield ◽

Machine Learning Models

Abstract Crop yield forecasting depends on many interactive factors including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. We propose a convolutional neural network (CNN) which uses the 1-dimentional convolution operation to capture the time dependencies of environmental variables. The proposed CNN, evaluated along with other machine learning models for winter wheat yield prediction in Germany, outperformed all other models tested. To address the seasonality, weekly features were used that explicitly take soil moisture and meteorological events into account. Our results indicated that nonlinear models such as deep learning models and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models and deep neural networks had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. Therefore, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). As such, our study indicates which variables have the most significant effect on winter wheat yield.

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Operating at the extreme: estimating the upper yield boundary of winter wheat production in commercial practice

Royal Society Open Science ◽

10.1098/rsos.191919 ◽

2020 ◽

Vol 7 (4) ◽

pp. 191919

Author(s):

Emily G. Mitchell ◽

Neil M. J. Crout ◽

Paul Wilson ◽

Andrew T. A. Wood ◽

Gilles Stupfler

Keyword(s):

Winter Wheat ◽

Crop Yield ◽

Agronomic Traits ◽

Maximum Yield ◽

Crop Protection ◽

Wheat Yield ◽

Statistical Evidence ◽

Extreme Value Analysis ◽

Value Analysis ◽

Winter Wheat Yield

Wheat farming provides 28.5% of global cereal production. After steady growth in average crop yield from 1950 to 1990, wheat yields have generally stagnated, which prompts the question of whether further improvements are possible. Statistical studies of agronomic parameters such as crop yield have so far exclusively focused on estimating parameters describing the whole of the data, rather than the highest yields specifically. These indicators include the mean or median yield of a crop, or finding the combinations of agronomic traits that are correlated with increasing average yields. In this paper, we take an alternative approach and consider high yields only. We carry out an extreme value analysis of winter wheat yield data collected in England and Wales between 2006 and 2015. This analysis suggests that, under current climate and growing conditions, there is indeed a finite upper bound for winter wheat yield, whose value we estimate to be 17.60 tonnes per hectare. We then refine the analysis for strata defined by either location or level of use of agricultural inputs. We find that there is no statistical evidence for variation of maximal yield depending on location, and neither is there statistical evidence that maximum yield levels are improved by high levels of crop protection and fertilizer use.

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Modelling future climate change impacts on winter wheat yield and water use: A case study in Guanzhong Plain, northwestern China

European Journal of Agronomy ◽

10.1016/j.eja.2020.126113 ◽

2020 ◽

Vol 119 ◽

pp. 126113 ◽

Cited By ~ 5

Author(s):

Qaisar Saddique ◽

De Li Liu ◽

Bin Wang ◽

Puyu Feng ◽

Jianqiang He ◽

...

Keyword(s):

Climate Change ◽

Winter Wheat ◽

Water Use ◽

Wheat Yield ◽

Climate Change Impacts ◽

Future Climate ◽

Northwestern China ◽

Future Climate Change ◽

Winter Wheat Yield

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Impacts of climate change and alternative adaptation options on winter wheat yield and water productivity in a dry climate in Central Europe

The Journal of Agricultural Science ◽

10.1017/s0021859612000093 ◽

2012 ◽

Vol 150 (5) ◽

pp. 537-555 ◽

Cited By ~ 52

Author(s):

S. THALER ◽

J. EITZINGER ◽

M. TRNKA ◽

M. DUBROVSKY

Keyword(s):

Climate Change ◽

Winter Wheat ◽

Water Demand ◽

Wheat Yield ◽

N Leaching ◽

Climate Scenarios ◽

Management Options ◽

Minimum Tillage ◽

Winter Wheat Yield ◽

Management Changes

SUMMARYThe main objective of the present crop simulation study was to determine the impact of climate change on the winter wheat production of a dry area situated in north-east Austria (Marchfeld region) based on the CERES-Wheat crop-growth simulation model associated with global circulation models (GCMs). The effects of some of the feasible regional- and farm-based adaptation measures (management options) on crop yield and water and nitrogen (N) balance under the climate scenarios were simulated. Climate scenarios were defined based on the ECHAM5, HadCM3 and NCAR PCM GCM simulations for future conditions (2021–50) as described in the Special Report on Emission Scenarios A1B (Nakicenovic & Swart 2000). The potential development, yield, water demand and soil N leaching were estimated for winter wheat and all of the defined climates (including rising CO2 levels) and management scenarios (soil cultivation, windbreaks and irrigation).The results showed that a warming of 2°C in the air temperature would shorten the crop-growing period by up to 20 days and would decrease the potential winter wheat yield on nearly all of the soil types in the region. Particularly, high-yield reductions were projected for light-textured soils such as Parachernozems. A change from ploughing to minimum tillage within the future scenario would lead to an increase of up to 8% of the mean yield of winter wheat. This effect mainly resulted from improved water supply to the crop, associated with higher soil water storage capacity and decrease of unproductive water losses. Hedgerows, which reduce the wind speed, were predicted to have particularly positive effects on medium and moderately fine-textured soils such as Chernozems and Fluvisols. With both management changes, regional mean-yield level can be expected to be +4% in comparison with no management changes in the future conditions. Compared with the baseline period, water demand for the potential yield of winter wheat would require 6–37 mm more water per crop season (area-weighted average). The highest water demand would be on medium-textured soils, which make up the largest amount of area in the study region. Additionally, the effects of snow accumulation near hedgerows would further increase the yield, but would also lead to higher N leaching rates. However, specific management options, such as minimum tillage and hedgerows, could contribute towards reducing the increasing water demand.

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