scholarly journals The Analysis of Wheat Yield Variability Based on Experimental Data from 2008–2018 to Understand the Yield Gap

Agriculture ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 32
Author(s):  
Elżbieta Wójcik-Gront ◽  
Marzena Iwańska ◽  
Agnieszka Wnuk ◽  
Tadeusz Oleksiak
Keyword(s):  
Winter Wheat ◽  
Plant Breeding ◽  
Management Practices ◽  
Wheat Yield ◽  
Yield Potential ◽  
Yield Variability ◽  
Mineral Fertilization ◽  
Yield Gap ◽  
Potential Yield ◽  
Winter Wheat Yield

Among European countries, Poland has the largest gap in the grain yield of winter wheat, and thus the greatest potential to reduce this yield gap. This paper aims to recognize the main reasons for winter wheat yield variability and shed the light on possible reasons for this gap. We used long-term datasets (2008–2018) from individual commercial farms obtained by the Laboratory of Economics of Seed and Plant Breeding of Plant Breeding and Acclimatization Institute (IHAR)-National Research Institute (Poland) and the experimental fields with high, close to potential yield, in the Polish Post-Registration Variety Testing System in multi-environmental trials. We took into account environment, management and genetic variables. Environment was considered through soil class representing soil fertility. For the crop management, the rates of mineral fertilization, the use of pesticides and the type of pre-crop were considered. Genotype was represented by the independent variable year of cultivar registration or year of starting its cultivation in Poland. The analysis was performed using the CART (Classification and Regression Trees). The winter wheat yield variability was mostly dependent on the amount of nitrogen fertilization applied, soil quality, and type of pre-crop. Genetic variable was also important, which means that plant breeding has successfully increased genetic yield potential especially during the last several years. In general, changes to management practices are needed to lower the variability of winter wheat yield and possibly to close the yield gap in Poland.

scholarly journals Plant Population and Fungicide Economically Reduced Winter Wheat Yield Gap in Kansas

2019 ◽  
Vol 111 (2) ◽  
pp. 650-665 ◽  
Author(s):  
Brent R. Jaenisch ◽  
Amanda de Oliveira Silva ◽  
Erick DeWolf ◽  
Dorivar A. Ruiz-Diaz ◽  
Romulo P. Lollato
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Plant Population ◽  
Yield Gap ◽  
Winter Wheat Yield

Multi-scale assessment of winter wheat yield gaps with an integrated evaluation framework in the Huang-Huai-Hai farming region in China

2019 ◽  
Vol 157 (6) ◽  
pp. 523-536
Author(s):  
S. Li ◽  
J. Liu ◽  
M. Shang ◽  
H. Jia ◽  
Y. Feng ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Evaluation Framework ◽  
High Yield ◽  
Limiting Factor ◽  
Yield Gap ◽  
Multi Scale ◽  
Winter Wheat Yield ◽  
Yield Gaps ◽  
Attainable Yield

AbstractQuantifying reasonable crop yield gaps and determining potential regions for yield improvement can facilitate regional plant structure adjustment and promote crop production. The current study attempted to evaluate the yield gap in a region at multi-scales through model simulation and farmer investigation. Taking the winter wheat yield gap in the Huang-Huai-Hai farming region (HFR) for the case study, 241 farmers’ fields in four typical high-yield demonstration areas were surveyed to determine the yield limitation index and attainable yield. In addition, the theoretical and realizable yield gap of winter wheat in 386 counties of the HFR was assessed. Results showed that the average field yield of the demonstration plots was 8282 kg/ha, accounting for 0.72 of the potential yield, which represented the highest production in the region. The HFR consists of seven sub-regions designated 2.1–2.7: the largest attainable yield gap existed in the 2.6 sub-region, in the southwest of the HFR, while the smallest was in the 2.2 sub-region, in the northwest of the HFR. With a high irrigated area rate, the yield gap in the 2.2 sub-region could hardly be reduced by increasing irrigation, while a lack of irrigation remained an important limiting factor for narrowing the yield gap in 2.3 sub-region, in the middle of the HFR. Therefore, a multi-scale yield gap evaluation framework integrated with typical field survey and crop model analysis could provide valuable information for narrowing the yield gap.

The influence of mineral fertilization on winter wheat yield and quality

2006 ◽  
Vol 34 (1) ◽  
pp. 429-432 ◽  
Author(s):  
Daniela Horvat ◽  
Zdenko Loncaric ◽  
Vladimir Vukadinovic ◽  
Georg Drezner ◽  
Blazenka Bertic ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Mineral Fertilization ◽  
Yield And Quality ◽  
Winter Wheat Yield

scholarly journals Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China

2020 ◽  
Vol 12 (5) ◽  
pp. 750 ◽  
Author(s):  
Juan Cao ◽  
Zhao Zhang ◽  
Fulu Tao ◽  
Liangliang Zhang ◽  
Yuchuan Luo ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Satellite Data ◽  
Large Scale ◽  
Wheat Yield ◽  
Yield Variability ◽  
Yield Prediction ◽  
Climate Data ◽  
Light Gradient ◽  
Source Data ◽  
Winter Wheat Yield

Wheat is a leading cereal grain throughout the world. Timely and reliable wheat yield prediction at a large scale is essential for the agricultural supply chain and global food security, especially in China as an important wheat producing and consuming country. The conventional approach using either climate or satellite data or both to build empirical and crop models has prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, socio-economic (SC) factors may also improve crop yield prediction, but their contributions need in-depth investigation, especially in regions with good irrigation conditions, sufficient fertilization, and pesticide application. Here, we performed the first attempt to predict wheat yield across China from 2001 to 2015 at the county-level by integrating multi-source data, including monthly climate data, satellite data (i.e., Vegetation indices (VIs)), and SC factors. The results show that incorporating all the datasets by using three machine learning methods (Ridge Regression (RR), Random Forest (RF), and Light Gradient Boosting (LightGBM)) can achieve the best performance in yield prediction (R2: 0.68~0.75), with the most individual contributions from climate (~0.53), followed by VIs (~0.45), and SC factors (~0.30). In addition, the combinations of VIs and climate data can capture inter-annual yield variability more effectively than other combinations (e.g., combinations of climate and SC, and combinations of VIs and SC), while combining SC with climate data can better capture spatial yield variability than others. Climate data can provide extra and unique information across the entire growing season, while the peak stage of VIs (Mar.~Apr.) do so. Furthermore, incorporating spatial information and soil proprieties into the benchmark models can improve wheat yield prediction by 0.06 and 0.12, respectively. The optimal wheat prediction can be achieved with approximately a two-month leading time before maturity. Our study develops timely and robust methods for winter wheat yield prediction at a large scale in China, which can be applied to other crops and regions.

Increasing Hard Winter Wheat Yield Potential via Synthetic Wheat: I. Path-Coefficient Analysis of Yield and Its Components

Crop Science ◽  
2012 ◽  
Vol 52 (5) ◽  
pp. 2014-2022 ◽  
Author(s):  
Jessica K. Cooper ◽  
A.M.H. Ibrahim ◽  
J. Rudd ◽  
Subas Malla ◽  
Dirk B. Hays ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Yield Potential ◽  
Path Coefficient ◽  
Path Coefficient Analysis ◽  
Synthetic Wheat ◽  
Winter Wheat Yield

Effects of different soil management practices on winter wheat yield and N losses on a dryland loess soil in China

Soil Research ◽  
2008 ◽  
Vol 46 (5) ◽  
pp. 455 ◽  
Author(s):  
Ke Jin ◽  
Stefaan De Neve ◽  
Bram Moeskops ◽  
Junjie Lu ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Surface Layer ◽  
Winter Wheat ◽  
Management Practices ◽  
Wheat Yield ◽  
Soil Management ◽  
Loess Soil ◽  
N Content ◽  
Total N ◽  
Nitrate N ◽  
Winter Wheat Yield

One of the most important problems in the Loess Plateau of China affecting sustainable agriculture is inefficient nutrient use. Field experiments were conducted to study the effects of different soil management practices on the nitrogen (N) dynamics and winter wheat yield on a loess soil in Luoyang, Henan province, China. The results showed that subsoiling with mulch (SS) consistently increased the yield of winter wheat primarily by better water harvest compared with conventional tillage (CT). The influence on yield of no till with mulch (NT) depended on the amount of precipitation. TC (2 crops per year) lowered the winter wheat yield mainly due to the unfavourable soil moisture conditions after growing peanut in summer; however, the harvested peanut gained an extra profit for the local farmer. N uptake by grain and straw and N export was highest for SS. Changes in frequency and intensity of tillage practice altered soil total N content and its distribution along the slope. SS and NT increased the N content of the surface layer (0–0.20 m) compared with CT, but there was no significant effect in deeper soil layers. Considerable amounts of nitrate-N were left in the profile 0–1.60 cm just after harvest under all treatments. The cumulative nitrate-N content to a depth of 1.60 m on average was 282 kg/ha, of which 56 kg/ha was in the layer 1.20–1.60 m, which is an indication of considerable nitrate leaching. From data of 7 consecutive years between 1999 and 2006, it could be concluded that SS resulted in the highest yield and total N content in the surface layer, and is the most sustainable tillage option for the circumstances of the study area.

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

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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