Identifying the impact of multi-hazards on crop yield—A case for heat stress and dry stress on winter wheat yield in northern China

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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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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Estimating Farm Wheat Yields from NDVI and Meteorological Data

Agronomy ◽

10.3390/agronomy11050946 ◽

2021 ◽

Vol 11 (5) ◽

pp. 946

Author(s):

Astrid Vannoppen ◽

Anne Gobin

Keyword(s):

Winter Wheat ◽

Crop Yield ◽

Meteorological Data ◽

Wheat Yield ◽

Model Performance ◽

Weather Conditions ◽

Current Data ◽

Monthly Precipitation ◽

Empirical Modelling ◽

Winter Wheat Yield

Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.

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Optimization of factors of managing productive processes of winter wheat in the Forest-steppe

Agricultural science and practice ◽

10.15407/agrisp7.02.044 ◽

2020 ◽

Vol 7 (2) ◽

pp. 44-54

Author(s):

M. M. Marenych ◽

V. F. Kaminsky ◽

C. Yu. Bulygin ◽

V. V. Hanhur ◽

I. V. Korotkova ◽

...

Keyword(s):

Winter Wheat ◽

Wheat Yield ◽

Nitrogen Fertilizers ◽

Maximal Increase ◽

Foliar Fertilization ◽

Combined Application ◽

Effi Ciency ◽

Crop Development ◽

Winter Wheat Yield ◽

The Impact

Aim. To study the impact of complex preparations, containing humic, fulvic and ulmic acids in combination with herbicides and nitrogen fertilizers, on winter wheat yield. Methods. Field, laboratory, statistical methods. Results. The impact of herbicides with different active substances and their mixtures with humic preparations Humifi eld and Foliar concentrate on weed infestation and productivity of Kryzhynka winter variety was investigated. It was determined that the application of herbicide Prima (400 g/ha) and the mixture of preparations Triger (25 g/ha) + Tomigan (0.5 l/ha) in combination with humic preparation Humifi eld in the dose of 200 g/ha had practically no impact on the crop productivity. A considerable increase in the yield, for instance, by 15.6–20.3 %, was observed in case of spraying the fi elds with the same preparation forms of herbicides in the tank mixture with humic stimulator 4R Foliar concentrate in the dose of 2.0 kg/ha. The application of humates in combination with nitrogen fertilizers with the purpose of optimizing the nutrition system for winter wheat via their introduction superfi cially and by spraying the leaf-stem mass of plants was studied. It was demonstrated that the application of the growth regulator 5R SoilBoost in the amount of 11 kg/ha in the mixture with 200 kg/ha of ammonia nitrate led to the increase of productivity for Smuhlianka and Slavna varieties by 11.2 and 8.5 % respectively, and double foliar application of 4R Foliar concentrate (2+2 kg/ha) in the mixture with ammonia nitrate – by 15.5 %. The maximal increase in productivity by 20–23 % was obtained after combined application of humic stimulators 5R SoilBoost (11 kg/ha) and 4R Foliar concentrate (2+2 kg/ha) on the background of ammonia nitrate (200 kg/ha of physical weight). The effi ciency of foliar fertilization for wheat fi elds of Kubus and Mulan varieties using the mixtures of humates and carbamide-ammonia mixture in different phases of crop development was analyzed. The application of such combinations also promoted the productivity increase by 10.0–21.4 %. Conclusions. The increase in productivity of Kryzhynka winter wheat variety by 0.64–0.84 t/ha was determined after spraying crop fi elds with the tank mixture of herbicides and humic stimulator 4R Foliar concentrate in the dose of 2.0 kg/ha. The effi ciency of optimizing the nutrition system of plants via separate or combined application of humic preparations, in particular, granulated 5R SoilBoost (11 kg/ha), superfi cially, and 4R Foliar concentrate (2 kg/ha+2 kg/ ha) in case of foliar fertilization for fi elds in different phases of crop development on the background of early spring introduction of ammonia nitrate (200 kg/ha) to frozen-thawed soil was proven. The increase in wheat productivity was observed in all variants of applying these mixtures. However, the maximal increase in the winter wheat yield was obtained due to the fertilization technology, envisaging the use of humates 5R SoilBoost and 4R Foliar concentrate on the background of ammonia nitrate. There was a noted increase in grain productivity of winter wheat varieties Kubus and Mulan by 0.50–0.94 and 0.41–1.08 t/ha respectively in case of superfi cial introduction of humic preparation 5R SoilBoost (11 kg/ha) and foliar fertilization of wheat fi elds with 4R Foliar concentrate (2+2 kg/ha) in combination with carbamide-ammonia mixture (200 + 100 kg/ha).

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Winter wheat yield and water use efficiency response to organic fertilization in northern China: A meta-analysis

Agricultural Water Management ◽

10.1016/j.agwat.2019.105934 ◽

2020 ◽

Vol 229 ◽

pp. 105934 ◽

Cited By ~ 4

Author(s):

Linlin Wang ◽

Qiang Li ◽

Jeffrey A. Coulter ◽

Junhong Xie ◽

Zhuzhu Luo ◽

...

Keyword(s):

Winter Wheat ◽

Water Use Efficiency ◽

Water Use ◽

Meta Analysis ◽

Wheat Yield ◽

Northern China ◽

Organic Fertilization ◽

Winter Wheat Yield ◽

Use Efficiency

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Climate Change Impacts on Winter Wheat Yield in Northern China

Advances in Meteorology ◽

10.1155/2019/2767018 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 3

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.

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The impact of variation in grain number and individual grain weight on winter wheat yield in the high yield potential environment of Ireland

European Journal of Agronomy ◽

10.1016/j.eja.2017.05.001 ◽

2017 ◽

Vol 87 ◽

pp. 40-49 ◽

Cited By ~ 21

Author(s):

Joseph P. Lynch ◽

Deirdre Doyle ◽

Shauna McAuley ◽

Fiona McHardy ◽

Quentin Danneels ◽

...

Keyword(s):

Winter Wheat ◽

Wheat Yield ◽

Grain Weight ◽

Yield Potential ◽

High Yield ◽

Grain Number ◽

Winter Wheat Yield ◽

The Impact ◽

High Yield Potential

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Impact of Preceding Crop and Cultural Practices on Rye Growth in Winter Wheat

Weed Technology ◽

10.1614/wt-09-014.1 ◽

2009 ◽

Vol 23 (4) ◽

pp. 564-568 ◽

Cited By ~ 10

Author(s):

Randy L. Anderson

Keyword(s):

Winter Wheat ◽

Spring Wheat ◽

Yield Loss ◽

Cultural Practices ◽

Wheat Yield ◽

Seeding Rate ◽

Starter Fertilizer ◽

Preceding Crop ◽

Winter Wheat Yield ◽

The Impact

Improving crop vigor can suppress growth of weeds present in the crop. This study examined the impact of preceding crop and cultural practices on rye growth in winter wheat. Preceding crops were soybean, spring wheat, and an oat/dry pea mixture. Two cultural treatments in winter wheat were also compared, referred to as conventional and competitive canopies. The competitive canopy differed from the conventional in that the seeding rate was 67% higher and starter fertilizer was banded with the seed. The study was conducted at Brookings, SD. Rye seed and biomass production differed fourfold among treatments, with winter wheat following oat/pea being most suppressive of rye growth. Rye produced 63 seeds/plant in winter wheat with a competitive canopy that followed oat/pea, contrasting with 273 seeds/plant in conventional winter wheat following spring wheat. Yield loss in winter wheat due to rye interference increased with rye biomass, but winter wheat was more tolerant of rye interference following oat/pea compared with the other preceding crops. Regression analysis indicated that winter wheat yield loss at the same rye biomass was threefold higher following spring wheat or soybean compared with oat/pea as a preceding crop. Winter wheat competitiveness and tolerance to rye can be improved by increasing the seeding rate, using a starter fertilizer, and growing winter wheat after an oat/pea mixture.

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