Statistical regression models for assessing climate impacts on crop yields: A validation study for winter wheat and silage maize in Germany

Gornott, Christoph; Wechsung, Frank

doi:10.1016/j.agrformet.2015.10.005

Cited by 86 publications

(80 citation statements)

References 48 publications

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“…Thus, risk management, such as a high yield stability of winter wheat, is of high importance. A possible explanation could be the relevance of yield stability for wheat production, especially with regards to the negative consequences of climate change, including more frequent early summer droughts, heavy rainfall events and late frosts caused by climate change [23,24]. Furthermore, climate change will also cause increased biotic stress pressure due to changes in the appearance of pests and diseases, which will also affect the yield variability of wheat negatively [25].…”

Section: Discussionmentioning

confidence: 99%

Yield Stability in Winter Wheat Production: A Survey on German Farmers’ and Advisors’ Views

Macholdt

Honermeier

2017

Agronomy

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Most of the available research studies have focused on the production of high grain yields of wheat and have neglected yield stability. However, yield stability is a relevant factor in agronomic practice and, therefore, is the focus of this comprehensive survey. The aim was to first describe the importance of yield stability as well as currently used practical management strategies that ensure yield stability in wheat production and secondly, to obtain potential research areas supporting yield stability in the complex system of agronomy. The target groups were German farmers with experience in wheat production and advisors with expertise in the field of wheat cultivation or research. A sample size of 615 completed questionnaires formed the data basis of this study. The study itself provides evidence that the yield stability of winter wheat is even more important than the amount of yield for a large proportion of farmers (48%) and advisors (47%). Furthermore, in the view of the majority of the surveyed farmers and advisors, yield stability is gaining importance in climate change. Data analysis showed that site adapted cultivar choice, favorable crop rotations and integrated plant protection are ranked as three of the most important agronomic management practices to achieve high yield stability of wheat. Soil tillage and fertilization occupied a middle position, whereas sowing date and sowing density were estimated with lower importance. However, yield stability is affected by many environmental, genetic and agronomic factors, which subsequently makes it a complex matter. Hence, yield stability in farming practice must be analyzed and improved in a systems approach.

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Section: Discussionmentioning

confidence: 99%

Yield Stability in Winter Wheat Production: A Survey on German Farmers’ and Advisors’ Views

Macholdt

Honermeier

2017

Agronomy

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“…The approach is “semi”empirical as known physiological influences are reflected in the exogenous variables, following the naming of Rahmstorf (). The concept was introduced in Wechsung, Lüttger, and Hattermann () and later successfully applied to German maize and winter wheat yields (Gornott & Wechsung, ). We extend the model by adding temperature–stress‐related variables, using more crops, applying it to 34 countries and providing two application cases: forecasting yield anomalies up to 2 months before harvest and gauging of yield losses under moderately increased temperatures.…”

Section: Introductionmentioning

confidence: 99%

Global evaluation of a semiempirical model for yield anomalies and application to within‐season yield forecasting

Schauberger

Gornott

Wechsung

2017

Global Change Biology

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Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two-thirds (63%-81%) of observed yield anomalies. Its out-of-sample performance (34%-55%) suggests a robust yield projection capacity when applied to unknown weather. Out-of-sample performance is lower when using remote sensing-derived yield data. The share of weather-driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%-84%). But the out-of-sample performance is lower (15%-42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2 months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within-season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high-quality yield monitoring and statistics as critical prerequisites to guide adaptation under climate change.

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“…As a result, various models (Gornott and Wechsung 2016;Hoetker 2007) have been put forward on smallholder responses to these situations, and are likely to drive the approaches to climate change adaptation and specific decisions regarding the mitigation plans (Le Dang et al 2014;Labbé et al 2016). But, significant actions by decision-makers and other key stakeholders may not be easily effected until there is a unified approach to the available knowledge and information regarding the actual state and trends at the downstream levels (Mertz et al 2009a).…”

Section: Introductionmentioning

confidence: 99%

Climate change adaptive capacity and smallholder farming in Trans-Mara East sub-County, Kenya

Simotwo

Mikalitsa

Wambua

2018

Geoenviron Disasters

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Background: At the centre of smallholders' adaptation is a need to understand their perceptions on key climatic scenarios so as to glean helpful information for key decision-making processes. In Kenya at the moment, downstream information regarding these circumstances remain scanty, with many smallholders being 'on their own', in spite of the imminent threats from shifting precipitation patterns, rising temperatures, and intensifying droughts. At the sub-national levels, potential impacts of these situations are likely to deepen due to extensive cases of land use transformations, habitat degradation, plummeting water resources capacity and common inter-ethnic conflicts, among other negative externalities. The study examined current climatic situations in Trans-Mara East sub-County, to the south-western part of Kenya, as well as the smallholders' perceptions about the situations, their adaptation levels and constraints thereof. Results: Pearson correlation coefficient, indicated a weak positive association between smallholder's perceptions and either their age, marital status, level of education, or livelihood streams (r ≤ 0.1; p ≥ 0.05, for all), unlike their climatic perceptions and farm sizes which showed a strong positive association (r = 0.430; p ≤ 0.01). Key desired adaptation options, improving crop varieties, livestock feeding techniques and crop diversification, topped their options, with destocking being least desired. Education levels (r = 0.229; p ≤ 0.05) and farm sizes (r = 0.534; p ≤ 0.01) had a positively significant association with adaptive capacity, in addition to a significantly weak, association between their adaptive capacity and both their individual's marital status (r = 0.154; p ≥ 0.05) and diversity of livelihood streams (r = 0.034; p ≥ 0.05). The analysis also showed a weak negative association between their adaptive capacity and age (r = − 0.026; p ≥ 0.05). Amid the key constraints which emerged include high cost of farm inputs, limited access to credit and market uncertainties, among others. Pearson correlation coefficient showed a significantly strong negative association between smallholders' constraints and both (r ≥ − 0.3; p ≤ 0.01) their level of education, and diversity of livelihood streams. A significantly strong positive association (r = 0.280; p ≤ 0.01) was identified between smallholder's age and constraints, while marital status and farm sizes both (r ≤ − 0.01; p ≥ 0.05) revealed weak non-significant negative association with the constraints.

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Statistical regression models for assessing climate impacts on crop yields: A validation study for winter wheat and silage maize in Germany

Cited by 86 publications

References 48 publications

Yield Stability in Winter Wheat Production: A Survey on German Farmers’ and Advisors’ Views

Yield Stability in Winter Wheat Production: A Survey on German Farmers’ and Advisors’ Views

Global evaluation of a semiempirical model for yield anomalies and application to within‐season yield forecasting

Climate change adaptive capacity and smallholder farming in Trans-Mara East sub-County, Kenya

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