Extreme weather events cause significant crop yield losses at the farm level in German agriculture

Schmitt, Jonas; Offermann, Frank; Söder, Mareike; Frühauf, Cathleen; Finger, Robert

doi:10.1016/j.foodpol.2022.102359

Cited by 88 publications

(47 citation statements)

References 51 publications

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“…However, below‐average rain and snow in fall 2021 and winter 2022 (Table 2) hindered the emergence of the CC; and a viable CC stand was not successful until the spring months of 2022. The highly variable weather conditions from year to year during this experiment appear to reflect the “new normal” weather conditions under the increasing climatic fluctuations in the region and elsewhere (D'Agostino & Schlenker, 2016; Schmitt et al., 2022). Thus, the study results of variable CC biomass production likely capture the variable weather and CC growth conditions in the region.…”

Section: Resultsmentioning

confidence: 90%

“…The highly variable weather conditions from year to year during this experiment appear to reflect the "new normal" weather conditions under the increasing climatic fluctuations in the region and elsewhere (D'Agostino & Schlenker, 2016;Schmitt et al, 2022). Thus, the study results of variable CC biomass production likely capture the variable weather and CC growth conditions in the region.…”

Section: Temperature and Precipitationmentioning

confidence: 80%

“…As alluded earlier, the extreme events (i.e., hailstorms and windstorms) are not uncommon under the increasing variable climatic conditions. Adaptation and adjustment of crop and CC management practices will be increasingly important under the new climate conditions (Elahi et al., 2022; Schmitt et al., 2022).…”

Section: Resultsmentioning

confidence: 99%

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Impact of planting green on soil properties under irrigated no‐till soybean

Stephens,

Blanco‐Canqui,

Knezevic

et al. 2023

Agrosystems Geosci & Env

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Planting green refers to the practice of planting a row crop into an actively growing cover crop (CC) and terminating it at or after row crop planting. Planting green could have more beneficial impacts on soil properties, erosion control, nutrient cycling, weed suppression, and other soil ecosystem services because it allows greater CC biomass accumulation than early‐terminated CC. The objectives of this 2‐year study were to evaluate the impact of planting green on soil properties (bulk density, wet aggregate stability, sorptivity, particulate organic matter, organic matter, nutrients, and others) and soybean (Glycine max L.) yield in an irrigated no‐till soybean system in south‐central Nebraska. Treatments were cereal rye (Secale cereale L.) CC terminated 2 weeks before planting (2WBP), CC terminated 2 weeks after planting (2WAP) soybean, and no CC. On average, CC produced 2.35 Mg ha−1 of biomass for 2WBP and 12.03 Mg ha−1 for 2WAP. Both 2WBP and 2WAP reduced N concentration by 48% (31.10 vs. 59.70 mg kg−1) but had no effect on other soil properties compared with no CC. Despite the abundant CC biomass production, terminating 2WAP had little to no effect on most soil properties in the short term (2 years). Wet aggregate stability increased as CC biomass production increased, while soil sorptivity (initial water infiltration) increased as wet aggregate stability increased. CC termination timing had inconsistent effects on soybean yield. In general, after 2 years, planting green had no effect on most soil properties or soybean yield, warranting long‐term studies on this topic.

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Section: Resultsmentioning

confidence: 90%

Section: Temperature and Precipitationmentioning

confidence: 80%

See 1 more Smart Citation

Impact of planting green on soil properties under irrigated no‐till soybean

Stephens,

Blanco‐Canqui,

Knezevic

et al. 2023

Agrosystems Geosci & Env

View full text Add to dashboard Cite

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“…Moreover, risk managers tend to be more interested in new risk management measures after a loss in order to avoid further damage (Weinstein, 1988). Given the more frequent and intense droughts causing yield losses (Schmitt et al. , 2022), the PU could further increase for more farmers.H9: Higher attitude of confidence ( Trust ) in index insurance products has a statistically significant positive effect on farmers' gradual PU.…”

Section: Resultsmentioning

confidence: 99%

“…Climate change puts pressure on the risk management of farms worldwide to secure agricultural incomes (Finger and El Benni, 2021). In Central Europe, catastrophic droughts and heat waves are occurring more frequently and affecting crop yields (Grillakis, 2019;Schmitt et al, 2022;Trnka et al, 2014). Index insurance is widely discussed to protect farmers from economic losses.…”

Section: Introductionmentioning

confidence: 99%

German farmers' perceived usefulness of satellite-based index insurance: insights from a transtheoretical model

Nordmeyer

Mußhoff

2023

AFR

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PurposeIndex insurance is promising to mitigate drought-related income losses in agriculture. To reduce the basis risk of index insurance, the integration of satellite data is of growing interest in research. The objective of this study is to obtain preliminary evidence regarding farmers' perceived usefulness (PU) of satellite-based index insurance.Design/methodology/approachBy modifying the transtheoretical model of change to a transtheoretical model of PU, German farmers' gradual PU of satellite-based index insurance was investigated.FindingsThe results show that the average farmer perceives satellite-based index insurance as useful. It can be particularly seen that a higher level of education in an agricultural context as well as higher trust in index insurance products increases farmers' gradual PU. Moreover, higher relative weather-related income losses increase farmers' gradual PU.Research limitations/implicationsIt is recommended to apply latent variables when conducting future investigations regarding farmers' PU.Originality/valueTo the best of the authors' knowledge, this is the first study to explore farmers' PU of upcoming satellite-based index insurance by modifying and applying the transtheoretical model in a new way.

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Crop yield estimation uncertainties at the regional scale for Saxony, Germany

Goihl

2024

Agronomy Journal

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In times of climate change and global population growth, agricultural yield forecasts play an increasingly important role. For example, predicting yields as early as possible in the event of a drought is crucial for decision‐makers in politics, government, and business. The aim of this study was to provide precise yield predictions at agricultural regions as early as possible with a minimum amount of weather data. Random forest models were used for this purpose. Although more than 290,000 datasets were available for analysis, all models tended to be heavily overfitting, which can be explained by the strong fragmentation of the input data by crop, region, and prediction time. The models reacted very differently to unknown datasets. It was found that the regionally trained models achieved lower (≥10%) relative root mean square errors (RRMSEs) than the supra‐regionally trained models. Rapeseed and barley achieved good predictions. Wheat had good potential, too. Corn, potatoes, and sugar beet achieved often too high RRMSEs. The results showed that targeted model selection for each region and an extension of the training time series could enable very good regional yield forecasts for rapeseed and cereals in the future.

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Extreme weather events cause significant crop yield losses at the farm level in German agriculture

Cited by 88 publications

References 51 publications

Impact of planting green on soil properties under irrigated no‐till soybean

Impact of planting green on soil properties under irrigated no‐till soybean

German farmers' perceived usefulness of satellite-based index insurance: insights from a transtheoretical model

Crop yield estimation uncertainties at the regional scale for Saxony, Germany

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