Agrometeorological Wheat Yield Prediction Models

Author(s):  
Nitin Bharadiya ◽  
Vivek Manekar
Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 258
Author(s):  
Anna Florence ◽  
Andrew Revill ◽  
Stephen Hoad ◽  
Robert Rees ◽  
Mathew Williams

Identification of yield deficits early in the growing season for cereal crops (e.g., Triticum aestivum) could help to identify more precise agronomic strategies for intervention to manage production. We investigated how effective crop canopy properties, including leaf area index (LAI), leaf chlorophyll content, and canopy height, are as predictors of winter wheat yield over various lead times. Models were calibrated and validated on fertiliser trials over two years in fields in the UK. Correlations of LAI and plant height with yield were stronger than for yield and chlorophyll content. Yield prediction models calibrated in one year and tested on another suggested that LAI and height provided the most robust outcomes. Linear models had equal or smaller validation errors than machine learning. The information content of data for yield prediction degraded strongly with time before harvest, and in application to years not included in the calibration. Thus, impact of soil and weather variation between years on crop phenotypes was critical in changing the interactions between crop variables and yield (i.e., slopes and intercepts of regression models) and was a key contributor to predictive error. These results show that canopy property data provide valuable information on crop status for yield assessment, but with important limitations.


2021 ◽  
Author(s):  
Sanbon Gosa ◽  
Amit Koch ◽  
Itamar Shenhar ◽  
Joseph Hirschberg ◽  
Dani Zamir ◽  
...  

To address the challenge of predicting tomato yields in the field, we used whole-plant functional phenotyping to evaluate water relations under well-irrigated and drought conditions. The genotypes tested are known to exhibit variability in their yields in wet and dry fields. The examined lines included two lines with recessive mutations that affect carotenoid biosynthesis, zeta z2083 and tangerine t3406, both isogenic to the processing tomato variety M82. The two mutant lines were reciprocally grafted onto M82 and multiple physiological characteristics were measured continuously, as well as before, during and after drought treatment in the greenhouse. A comparative analysis of greenhouse and field yields showed that the whole-canopy stomatal conductance (gsc) in the morning and cumulative transpiration (CT) were strongly correlated with field measurements of total yield (TY: r2 = 0.9 and 0.77, respectively) and plant vegetative weight (PW: r2 = 0.6 and 0.94, respectively). Furthermore, the minimum CT during drought and the rate of recovery when irrigation was resumed were both found to predict resilience. Keywords: drought tolerance, functional genomic mapping, functional phenotyping, physiological trait, time-series measurements, tomato, yield prediction, yield-prediction models


Author(s):  
Vikas Lamba ◽  
Susheela Hooda ◽  
Rakesh Ahuja ◽  
Amandeep Kaur

2008 ◽  
Vol 8 (3) ◽  
pp. 510-515 ◽  
Author(s):  
S. Bazgeer ◽  
R.K. Mahey ◽  
S.S. Sidhu ◽  
P.K. Sharma ◽  
A. Sood ◽  
...  

2020 ◽  
Vol 12 (6) ◽  
pp. 1024 ◽  
Author(s):  
Yan Zhao ◽  
Andries B Potgieter ◽  
Miao Zhang ◽  
Bingfang Wu ◽  
Graeme L Hammer

Accurate prediction of crop yield at the field scale is critical to addressing crop production challenges and reducing the impacts of climate variability and change. Recently released Sentinel-2 (S2) satellite data with a return cycle of five days and a high resolution at 13 spectral bands allows close observation of crop phenology and crop physiological attributes at field scale during crop growth. Here, we test the potential for indices derived from S2 data to estimate dryland wheat yields at the field scale and the potential for enhanced predictability by incorporating a modelled crop water stress index (SI). Observations from 103 study fields over the 2016 and 2017 cropping seasons across Northeastern Australia were used. Vegetation indices derived from S2 showed moderately high accuracy in yield prediction and explained over 70% of the yield variability. Specifically, the red edge chlorophyll index (CI; chlorophyll) (R2 = 0.76, RMSE = 0.88 t/ha) and the optimized soil-adjusted vegetation index (OSAVI; structural) (R2 = 0.74, RMSE = 0.91 t/ha) showed the best correlation with field yields. Furthermore, combining the crop model-derived SI with both structural and chlorophyll indices significantly enhanced predictability. The best model with combined OSAVI, CI and SI generated a much higher correlation, with R2 = 0.91 and RMSE = 0.54 t/ha. When validating the models on an independent set of fields, this model also showed high correlation (R2 = 0.93, RMSE = 0.64 t/ha). This study demonstrates the potential of combining S2-derived indices and crop model-derived indices to construct an enhanced yield prediction model suitable for fields in diversified climate conditions.


2020 ◽  
Vol 281 ◽  
pp. 107827 ◽  
Author(s):  
Magdalena Gos ◽  
Jaromir Krzyszczak ◽  
Piotr Baranowski ◽  
Małgorzata Murat ◽  
Iwona Malinowska

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