scholarly journals Assessment of water-limited winter wheat yield potential at spatially contrasting sites in Ireland using a simple growth and development model

2017 ◽  
Vol 56 (1) ◽  
pp. 65-76 ◽  
Author(s):  
J.P. Lynch ◽  
R. Fealy ◽  
D. Doyle ◽  
L. Black ◽  
J. Spink
Keyword(s):  
Winter Wheat ◽  
Growth And Development ◽  
Wheat Yield ◽  
Yield Potential ◽  
Weather Data ◽  
Minor Amount ◽  
Accurate Estimation ◽  
Area Index ◽  
Climatic Environment ◽  
Winter Wheat Yield

AbstractAlthough Irish winter wheat yields are among the highest globally, increases in the profitability of this crop are required to maintain its economic viability. However, in order to determine if efforts to further increase Irish wheat yields are likely to be successful, an accurate estimation of the yield potential is required for different regions within Ireland. A winter wheat yield potential model (WWYPM) was developed, which estimates the maximum water-limited yield achievable, within the confines of current genetic resources and technologies, using parameters for winter wheat growth and development observed recently in Ireland and a minor amount of daily meteorological input (maximum and minimum daily temperature, total daily rainfall and total daily incident radiation). The WWYPM is composed of three processes: (i) an estimation of potential green area index, (ii) an estimation of light interception and biomass accumulation and (iii) an estimation of biomass partitioning to grain yield. Model validation indicated that WWYPM estimations of water-limited yield potential (YPw) were significantly related to maximum yields recorded in variety evaluation trials as well as regional average and maximum farm yields, reflecting the model’s sensitivity to alterations in the climatic environment with spatial and seasonal variations. Simulations of YPw for long-term average weather data at 12 sites located at spatially contrasting regions of Ireland indicated that the typical YPw varied between 15.6 and 17.9 t/ha, with a mean of 16.7 t/ha at 15% moisture content. These results indicate that the majority of sites in Ireland have the potential to grow high-yielding crops of winter wheat when the effects of very high rainfall and other stresses such as disease incidence and nutrient deficits are not considered.

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

Estimation of the average district winter wheat yield based on satellite and ground meteorological data

2020 ◽  
Vol 3 ◽  
pp. 103-121
Author(s):  
A.D. Kleschenko ◽  
◽  
O.V. Savitskaya ◽  
S.A. Kosyakin ◽  
◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Wheat Yield ◽  
Condition Index ◽  
Area Index ◽  
The North ◽  
Independent Information ◽  
Winter Wheat Yield

The research results of the dependence of the average district winter wheat yield on satellite and ground meteorological information for the subjects of the North Caucasian and Volga UGMS are presented. The following satellite indices were used in the work: NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index) and LAI (Leaf Area Index). The method of interpolation of inverse weighted squares of distances for obtain a set of meteorological parameters for districts there were no weather stations was used. Districts for taking into account agroclimatic conditions were combined into groups using Shashko's Agroclimatic Regionalization method. The selection of parameters that have the greatest impact on the yield was carried out using the correlation-regression analysis method. The corresponding regression models were obtained for the researched regions of the Russian Federation. Verification of the obtained models on dependent and independent information showed a fairly good result. Keywords: NDVI, LAI, interpolation, Shashko's Agroclimatic Regionalization, average district yield, meteorological information Tab. 5. Fig. 7. Ref. 20.

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>

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

2017 ◽  
Vol 87 ◽  
pp. 40-49 ◽  
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

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.

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