Potential effects of perfect seasonal climate forecasting on agricultural markets, welfare and land use: A case study of Spain

Choi, Hyung Sik; Schneider, Uwe A.; Rasche, Livia; Cui, J.; Schmid, Erwin; Held, Hermann

doi:10.1016/j.agsy.2014.10.007

Cited by 20 publications

(18 citation statements)

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“…(2015) support this idea in an economic study from Spain on cotton, two vegetable crops, two grain crops, and animal calories. They concluded that revenue increases not only were positive for almost all crops using proactive measures, but were also remarkably higher (between 1.5 and 5 times) compared to conservative approaches, which also often had revenue losses (Choi et al, 2015).…”

Section: Forecast Effectivenessmentioning

confidence: 99%

The development of seasonal climate forecasting for agricultural producers

Klemm

McPherson

2017

Agricultural and Forest Meteorology

108

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Body (7634 words) 1 Introduction Seasonal climate forecasts are used by crop producers for long-term decisions, for example planting and harvest timing, field fertilization, or water management, among others. To review the literature, the main methods used were keyword searches in publisher-unaffiliated databases such as Web of Knowledge and in publication libraries of institutions known for their interdisciplinary work in climate forecasting and agriculture, such as the Southeast Climate Consortium and the International Research Institute for Climate and Society. Section 1 outlines a brief history on the topic. Section 2 explores singlemodel and ensemble forecasts. Section 3 reviews measures of skill and utility to assess the value of seasonal climate forecasts. Section 4 puts seasonal climate forecasts in context with other agricultural decision factors. Section 5 looks at tailored seasonal climate forecasts and seasonal climate forecasts from an agricultural perspective, while exploring communication challenges between forecast provider and forecast user, and highlights how agricultural decision-making differs depending on the crop type and planning horizon. Lastly, section 6 explores end-to-end concepts.

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Section: Forecast Effectivenessmentioning

confidence: 99%

The development of seasonal climate forecasting for agricultural producers

Klemm

McPherson

2017

Agricultural and Forest Meteorology

108

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“…Advancing the understanding on how climate variability and extremes influence crop production provides the basis to develop an integrated seasonal crop yield forecasting system 1 – 4 . Seasonal climate forecasts represent an important tool to inform end-users with greater accuracy 5 – 8 by also providing a quantification of uncertainties, a key aspect in the decision-making process 9 , 10 . Even though additional value for agricultural applications in several regions of the world has been shown 11 , seasonal climate forecasts have been faced with numerous challenges to adequately respond to the end-users expectations in impact sectors such as agriculture.…”

Section: Introductionmentioning

confidence: 99%

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast

Ceglar

Toreti

Prodhomme

et al. 2018

Sci Rep

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Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

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“…Finally, a change in the crop mix as a management strategy was not considered in this analysis as wheat is the main crop grown in this region and farmers would normally plant only once a year due to water limitations. But, as found in other studies (Sultan et al 2010) a change in the crop mix can be used to mitigate the negative effect of climate variability and can also lead to a change in the value of climate information (Choi et al 2015).…”

Section: Discussionmentioning

confidence: 97%