Evaluation of ridge regression for country-wide prediction of genotype-specific grain yields of wheat

Herrera, Juan M.; Häner, Lilia Levy; Holzkämper, Annelie; Pellet, Didier

doi:10.1016/j.agrformet.2017.12.263

Cited by 13 publications

(7 citation statements)

References 54 publications

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“…While there is a conceptual understanding on how phenology may be influenced by climate conditions, it is challenging to provide quantitative estimates of the magnitude of these shifts (Richardson et al, 2013;Herrera et al, 2018). Field phenotyping with PhenoCams is a low cost, fully automated approach to gather very high temporal resolution data of an area sufficient for experimental trials.…”

Section: Investigating Interactions Of Growth Harvest Traits and Phenology With Phenocamsmentioning

confidence: 99%

PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

et al. 2020

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Understanding the interaction of plant growth with environmental conditions is crucial to increase the resilience of current cropping systems to a changing climate. Here, we investigate PhenoCams as a high-throughput approach for field phenotyping experiments to assess growth dynamics of many different genotypes simultaneously in high temporal (daily) resolution. First, we develop a method that extracts a daily phenological signal that is normalized for the different viewing geometries of the pixels within the images. Second, we investigate the extraction of the in season traits of early vigor, leaf area index (LAI), and senescence dynamic from images of a soybean (Glycine max) field phenotyping experiment and show that it is possible to rate early vigor, senescence dynamics, and track the LAI development between LAI 1 and 4.5. Third, we identify the start of green up, green peak, senescence peak, and end of senescence in the phenological signal. Fourth, we extract the timing of these points and show how this information can be used to assess the impact of phenology on harvest traits (yield, thousand kernel weight, and oil content). The results demonstrate that PhenoCams can track growth dynamics and fill the gap of high temporal monitoring in field phenotyping experiments.

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Section: Investigating Interactions Of Growth Harvest Traits and Phenology With Phenocamsmentioning

confidence: 99%

PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

et al. 2020

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“…Therefore, it seems that stable variety mixtures would require to combine varieties that respond differently to environmental variations, for example, with different susceptibilities to abiotic stresses such as heat, water limitation, soil structure, solar radiation, and so forth. In practice, this requires to be able to properly characterize each environment and its limiting factors (Costa‐Neto et al., 2020; Xu, 2016), but also to be able to predict how will each variety perform in that said environment (Costa‐Neto et al., 2022; Herrera et al., 2018).…”

Section: Discussionmentioning

confidence: 99%

“…The performances of single varieties were obtained by going through the trials of the national variety testing program (Herrera et al., 2018). We gathered the data for the years 2018/2019 and 2019/2020.…”

Section: Methodsmentioning

confidence: 99%

Asynchrony is more important than genetic distance in driving yield stability in wheat variety mixtures

Stefan,

Fossati,

Camp

et al. 2023

Crop Science

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In the face of climate change, improving yield stability is critical for food production systems. Increasing diversity in agricultural systems can be a way to stabilize yield across time and/or space. This can be done with mixtures of varieties, which represent a practical way to introduce diversity at the genotype level in agricultural fields. However, in Switzerland, wheat variety mixtures are still rarely used, due to a lack of understanding of the processes driving yield benefits and, consequently, no clear rules as to which varieties to combine. In this study, we used results from a Swiss wheat variety mixture trial to investigate the drivers of mixture productivity and stability. We hypothesized that mixtures composed of more different varieties—in terms of genetic distance but also of morphological, agronomic, or synchronicity aspects—would be more productive and/or stable. We did not find any effects of genetic distance on mixture productivity or stability, but we did observe an increase in mixture stability with reduced synchrony of the components, that is, when the components reacted differently to environmental conditions. This result was only significant in the first year of the trial, which was less productive due to more stressful conditions. This study shows that when combining asynchronous varieties, wheat variety mixtures have the potential to buffer possible yield losses due to external stressors during suboptimal years. While more research is needed to confirm this result, variety mixtures nonetheless represent a promising solution to sustainably increase the stability of wheat production in Switzerland.

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“…In research on ridge regression for grain yields prediction [13], identify the potential and limitations for the use of the factors derived and ridge regression to predict the performance. Results have shown that prediction accuracies depend on the variables, and there are statistical models (in this case ridge regression) suitable for predicting performance in the areas and highlights limitations associated with the crop and environmental data in the model.…”

Section: Related Workmentioning

confidence: 99%

Food Price Prediction Using Time Series Linear Ridge Regression with The Best Damping Factor

Yasmina¹,

Wibowo²

2021

Adv. sci. technol. eng. syst. j.

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Forecasting food prices play an important role in livestock and agriculture to maximize profits and minimizing risks. An accurate food price prediction model can help the government which leads to optimization of resource allocation. This paper uses ridge regression as an approach for forecasting with many predictors that are related to the target variable. Ridge regression is an expansion of linear regression. It's fundamentally a regularization of the linear regression model. Ridge regression uses the damping factor (λ) as a scalar that should be learned, normally it will utilize a method called cross-validation to find the value. But in this research, we will calculate the damping factor/ridge regression in the ridge regression (RR) model firsthand to minimize the running time used when using cross-validation. The RR model will be used to forecast the food price time-series data. The proposed method shows that calculating the damping factor/regression estimator first results in a faster computation time compared to the regular RR model and also ANFIS.

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Evaluation of ridge regression for country-wide prediction of genotype-specific grain yields of wheat

Cited by 13 publications

References 54 publications

PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

Asynchrony is more important than genetic distance in driving yield stability in wheat variety mixtures

Food Price Prediction Using Time Series Linear Ridge Regression with The Best Damping Factor

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