Yield Estimation of Winter Wheat in Pre-harvest Season by Satellite Imagery Based Regression Models

Early crop yield estimates could provide up-to-date information on supply, demand, stocks, and export availability through which governing bodies can make better agricultural management plans. This study aims to develop a yield model estimating pre-harvest winter wheat yield at both tillering and flowering stages using a multiple linear regression approach based on the relationship between actual yield and satellite derived crops’ phenological parameters. Four crop parameters (NDVI, Cumulative NDVI, LAI and FPAR) were regressed in combination to find the best applicable model. Regression results showed that correlations for all models among the variables of the flowering period are higher than that of tillering (0.63>0.53). The mean RMSE’s of the observed vs predicted yields for tillering period was 645.9 kg ha-1 and 574.5 kg ha-1 for flowering period. The optimal developed model which consists of NDVI and CNDVI variables provided 76% and 79% of predicting accuracy 3 and 1.5 months before harvest respectively.

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The affectivity using of the winter wheat by detailed methoda of the structure harvest

Bulletin of Kharkiv National Agrarian University named after V.V. Dokychaiev. The series “Crop production, selection and seed production, fruit and vegetable growing” ◽

10.35550/visnykagro2019.21.006 ◽

2019 ◽

Vol 2019 (2) ◽

pp. 6-14

Author(s):

G.F. Оlkhovskyi ◽

М.А. Bobro ◽

О.F. Chechui

Keyword(s):

Winter Wheat ◽

Key Words ◽

Wheat Yield ◽

Wheat Crop ◽

Yield Structure ◽

Winter Wheat Yield ◽

Individual Features ◽

The Individual ◽

The Relationship

The most difficult but most informative method of determining the structure of winter wheat yield with the use of large bunches of samples is presented. The role of the stem in the formation of allthe elements of winter wheat yield structure is determined. The advantage of our method is that it allows to get deeper information about the structure of the wheat crop, as it reveals the relationship between the individual elements of the crop structure and shows the amplitude of fluctuations in individual features of thewheat crop structure. Key words: winter wheat, yield structure, stem, weight and number of grains.

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Comparing a random forest based prediction of winter wheat yield to historical production potential

10.5194/egusphere-egu2020-138 ◽

2020 ◽

Author(s):

Yannik Roell ◽

Amélie Beucher ◽

Per Møller ◽

Mette Greve ◽

Mogens Greve

Keyword(s):

Random Forest ◽

Winter Wheat ◽

Wheat Yield ◽

Yield Potential ◽

Production Potential ◽

Yield Model ◽

Spatiotemporal Trends ◽

Winter Wheat Yield ◽

Historical Production ◽

Yield Map

<p>Predicting wheat yield is crucial due to the importance of wheat across the world. When modeling yield, the difference between potential and actual yield consistently changes because of technology. Considering historical yield potential would help determine spatiotemporal trends in agricultural development. Comparing current and historical production in Denmark is possible because production has been documented throughout history. However, the current winter wheat yield model is solely based on soil. The aim of this study was to generate a new Danish winter wheat yield map and compare the results to historical production potential. Utilizing random forest with soil, climate, and topography variables, a winter wheat yield map was generated from 876 field trials carried out from 1992 to 2018. The random forest model performed better than the model based only on soil. The updated national yield map was then compared to production potential maps from 1688 and 1844. While historical time periods are characterized by numerous low production potential areas and few highly productive areas, present-day production is evenly distributed between low and high production. Advances in technology and farm practices have exceeded historical yield predictions. Thus, modeling current yield could be unreliable in future years as technology progresses.</p>

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Winter Wheat Cultivar Recommendation Based on Expected Environment Productivity

Agriculture ◽

10.3390/agriculture11060522 ◽

2021 ◽

Vol 11 (6) ◽

pp. 522

Author(s):

Marzena Iwańska ◽

Jakub Paderewski ◽

Michał Stępień ◽

Paulo Canas Rodrigues

Keyword(s):

Environmental Factors ◽

Winter Wheat ◽

Mixed Model ◽

Wheat Yield ◽

Analysis Of Covariance ◽

Hydrothermal Coefficient ◽

Soil Suitability ◽

Winter Wheat Yield ◽

Wide Adaptation ◽

The Relationship

We used 5 years of data from multi-environmental trials conducted in Poland to assess average winter wheat yield based on selected environmental factors to recommend cultivars depending on their performance in environments of different productivity. Average expected yields in particular environments were calculated using a model based on analysis of covariance (ANCOVA), which describes the relationship between winter wheat yield and environmental factors of soil suitability and pH, drought length and Selyaninov’s Hydrothermal Coefficient (HTC) in 10-day periods. The cultivar performance was evaluated using linear regression. The cultivar yield estimated by the mixed model was considered the dependent variable, whereas the environmental mean yields, estimated by ANCOVA, were considered independent variables. The cultivars were ranked according to the estimated yield in environments of determined average wheat productivity. Higher yielding cultivars were divided into two groups: widely and narrowly adapted cultivars, which were then recommended. The novelty of this study stems from the consideration of the environmental productivity in the recommendation process, the indication of widely adapted cultivars to be grown in a broad range of productivity sites and the selection of cultivars with narrow adaptation, which may outperform cultivars of wide adaptation in homogeneous fields. This study confirmed the importance of soil suitability and HTC for winter wheat yield. Direct application of our results is possible in Poland and in other countries with similar conditions.

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

Sustainability ◽

10.3390/su12010135 ◽

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.

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Evaluation of the impact of fertilizers and seed quality on winter wheat yield

Biometrical Letters ◽

10.1515/bile-2017-0004 ◽

2017 ◽

Vol 54 (1) ◽

pp. 61-76

Author(s):

Marzena Iwańska ◽

Zbigniew Laudański ◽

Tadeusz Oleksiak

Keyword(s):

Winter Wheat ◽

Seed Quality ◽

Wheat Yield ◽

Fertilization Rate ◽

Mineral Fertilization ◽

Winter Wheat Yield ◽

The Mean ◽

The Impact ◽

Fertilizer Rates ◽

Made In

Summary The aim of the study was to evaluate the effect of mineral fertilization and seed quality on the yield of winter wheat in production conditions. This assessment is made in terms of the expected probabilities of success in relation to the yield of analyzed cultivars, taking into account the interaction of the factors considered. Analyses were performed on data from 3815 fields. The impact of fertilizers and seed quality was evaluated using logistic regression. Grain yield was transformed into a binomial variable, where values were divided into two classes, i.e. below the mean and above the mean. The results of the analysis proved a significant effect of fertilization rate, which was modified by different seed quality. The highest probability of obtaining yields above the mean was observed for pre-basic and basic seed quality at high fertilizer rates.

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Estimation of Winter Wheat Yield from UAV-Based Multi-Temporal Imagery Using Crop Allometric Relationship and SAFY Model

Drones ◽

10.3390/drones5030078 ◽

2021 ◽

Vol 5 (3) ◽

pp. 78

Author(s):

Yang Song ◽

Jinfei Wang ◽

Bo Shan

Keyword(s):

Winter Wheat ◽

Wheat Yield ◽

Yield Prediction ◽

Yield Estimation ◽

Allometric Relationship ◽

Area Index ◽

Crop Height ◽

Winter Wheat Yield ◽

The Relationship ◽

Height Model

Crop yield prediction and estimation play essential roles in the precision crop management system. The Simple Algorithm for Yield Estimation (SAFY) has been applied to Unmanned Aerial Vehicle (UAV)-based data to provide high spatial yield prediction and estimation for winter wheat. However, this crop model relies on the relationship between crop leaf weight and biomass, which only considers the contribution of leaves on the final biomass and yield calculation. This study developed the modified SAFY-height model by incorporating an allometric relationship between ground-based measured crop height and biomass. A piecewise linear regression model is used to establish the relationship between crop height and biomass. The parameters of the modified SAFY-height model are calibrated using ground measurements. Then, the calibrated modified SAFY-height model is applied on the UAV-based photogrammetric point cloud derived crop height and effective leaf area index (LAIe) maps to predict winter wheat yield. The growing accumulated temperature turning points of an allometric relationship between crop height and biomass is 712 °C. The modified SAFY-height model, relative to traditional SAFY, provided more accurate yield estimation for areas with LAI higher than 1.01 m2/m2. The RMSE and RRMSE are improved by 3.3% and 0.5%, respectively.

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Dependence of winter wheat yield on the content and composition of organic matter of typical chernozem

BIO Web of Conferences ◽

10.1051/bioconf/20213202008 ◽

2021 ◽

Vol 32 ◽

pp. 02008

Author(s):

Nina Masyutenko ◽

Alexey Kuznetsov ◽

Maxim Masyutenko ◽

Tatiana Pankova

Keyword(s):

Organic Matter ◽

Soil Organic Matter ◽

Winter Wheat ◽

Wheat Yield ◽

Optimal Parameters ◽

Hydrothermal Conditions ◽

Typical Chernozem ◽

Winter Wheat Yield ◽

The Relationship

The article is devoted to the study of the relationship and the determination of the dependence of winter wheat yield on the content and composition of the organic matter of typical chernozem, the determination of its optimal parameters in different by hydrothermal conditions years. The research was carried out in 2018-2019 in a typical heavy-loamy chernozem in the experimental field of the Kursk FARC. Coupled studies of the yield of winter wheat Synthetics and indicators of soil organic matter in the topsoil were carried out on 30-meter sites during the harvest period. The range of fluctuations in the content and composition of organic matter in the soil, the yield of winter wheat at the studied sites allowed us to apply information and logical analysis within the framework of the analyzed soil-plant system. A high dependence of winter wheat yield on the content and composition of soil organic matter was established, in 2018 the coefficients of information transfer efficiency varied from 0.23 to 0.17, in 2019 from 0.32 to 0.18. It was found that in dry 2019 the dependence of winter wheat yield on the biogenicity of mobile humus substances, the lability of humus, and microbial biomass has increased, and the influence of humus has decreased. It is established that in the studied years the significance of the parameters for the formation of winter wheat yield remained, the degree of their influence and sometimes the nature of the direction of the relationship changed. The optimal parameters of indicators of soil organic matter in the arable layer of chernozem typical for obtaining the yield of winter wheat Synthetic 5.45-7.24 t/ha in a favorable by hydrothermal conditions year and 4.78-7.19 t/ha in a dry year were established.

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