Effects of diurnal temperature range and drought on wheat yield in Spain

Hernandez-Barrera, Sara; Rodríguez-Puebla, Concepción; Challinor, Andrew J.

doi:10.1007/s00704-016-1779-9

Cited by 41 publications

(39 citation statements)

References 95 publications

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“…On the other hand, crop models describing the biological processes are one of the existing tools used to assess crop productivity, e.g. CERES (Crop Environment REsource Synthesis) models (Capa-Morocho et al, 2016;Hlavinka et al, 2010) and AquaCrop (Paredes et al, 2016;Vergni et al, 2015). These crop models are important tools in agrometeorological studies as they are able to compute irrigation requirements and yield simulations, and they have been particularly useful for assessing the impacts of climate change on agricultural productions (Leng and Hall, 2019;Hlavinka et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic modelling of the dependence between rainfed crops and drought hazard

Ribeiro

Russo

Gouveia

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. Extreme weather events, such as droughts, have been increasingly affecting the agricultural sector, causing several socio-economic consequences. The growing economy requires improved assessments of drought-related impacts in agriculture, particularly under a climate that is getting drier and warmer. This work proposes a probabilistic model that is intended to contribute to the agricultural drought risk management in rainfed cropping systems. Our methodology is based on a bivariate copula approach using elliptical and Archimedean copulas, the application of which is quite recent in agrometeorological studies. In this work we use copulas to model joint probability distributions describing the amount of dependence between drought conditions and crop yield anomalies. Afterwards, we use the established copula models to simulate pairs of yield anomalies and drought hazard, preserving their dependence structure to further estimate the probability of crop loss. In the first step, we analyse the probability of crop loss without distinguishing the class of drought, and in the second step we compare the probability of crop loss under drought and non-drought conditions. The results indicate that, in general, Archimedean copulas provide the best statistical fits of the joint probability distributions, suggesting a dependence among extreme values of rainfed cereal yield anomalies and drought indicators. Moreover, the estimated conditional probabilities suggest that when drought conditions are below moderate thresholds, the risk of crop loss increases between 32.53 % (cluster 1) and 32.6 % (cluster 2) in the case of wheat and between 31.63 % (cluster 2) and 55.55 % (cluster 2) in the case of barley. From an operational point of view, the results aim to contribute to the decision-making process in agricultural practices.

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Section: Introductionmentioning

confidence: 99%

Probabilistic modelling of the dependence between rainfed crops and drought hazard

Ribeiro

Russo

Gouveia

et al. 2019

Nat. Hazards Earth Syst. Sci.

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“…The models are useful for analyzing the impact of climate change and for adapting strategies in the event of potential crop‐yield losses (FAO, ; IPCC, ). In an earlier work (Hernandez‐Barrera et al ., , hereafter HB2016), we proposed a crop model to characterize wheat‐yield variability based on historical data of seasonal drought and diurnal temperature range (DTR). We extrapolated this model to predict wheat yield in Spain for the 21st century using regional precipitation and temperatures produced by general circulation models (GCMs) and projected a reduction of about 32% over the country.…”

Section: Introductionmentioning

confidence: 99%

Wheat yield in Spain and associated solar radiation patterns

Hernandez-Barrera

Rodríguez-Puebla

2017

Intl Journal of Climatology

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Climate variability and change can have important impacts for crop production. Therefore, the aim of this study is to investigate projections of the wheat yield in an increasingly warm climate. To address our objectives, we determined relationships between wheat yield in Spain and large‐scale variables. Partial least squares regression was applied to determine the modes of the climate variables that drive wheat‐yield variability, revealing a significant influence of surface solar radiation. Based on seasonal patterns of solar radiation, we determine models to estimate inter‐annual wheat‐yield variability. We find that the performance of the models based on solar radiation is better than that of earlier studies based on temperatures and precipitation variables. In this way, we use simulations of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to project wheat‐yield trend under warming climate by implementing direct statistical downscaling. The expected range of projected wheat yield trend for 21st century indicates decreases of about 6–8% across Spain. The suggested models could be applied for adaptation and planning.

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“…For the Iberian Peninsula, most studies that do not consider adaptation measures predict a reduction in crop rainfed dry matter yields for all climate change projections (e.g., 20%: Ciscar et al, 2014), being especially severe for the end of the 21st century (e.g., wheat: Hernandez‐Barrera et al, 2017) due to increased drought duration and intensity.…”

Section: Resultsmentioning

confidence: 99%

Modeling Regional Effects of Climate Change on Soil Organic Carbon in Spain

et al. 2018

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Soil organic C (SOC) stock assessments at the regional scale under climate change scenarios are of paramount importance in implementing soil management practices to mitigate climate change. In this study, we estimated the changes in SOC sequestration under climate change conditions in agricultural land in Spain using the RothC model at the regional level. Four Intergovernmental Panel on Climate Change (IPCC) climate change scenarios (CGCM2-A2, CGCM2-B2, ECHAM4-A2, and ECHAM4-B2) were used to simulate SOC changes during the 2010 to 2100 period across a total surface area of 2.33 × 10 km. Although RothC predicted a general increase in SOC stocks by 2100 under all climate change scenarios, these SOC sequestration rates were smaller than those under baseline conditions. Moreover, this SOC response differed among climate change scenarios, and in some situations, some losses of SOC occurred. The greatest losses of C stocks were found mainly in the ECHAM4 (highest temperature rise and precipitation drop) scenarios and for rainfed and certain woody crops (lower C inputs). Under climate change conditions, management practices including no-tillage for rainfed crops and vegetation cover for woody crops were predicted to double and quadruple C sequestration rates, reaching values of 0.47 and 0.35 Mg C ha yr, respectively.

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Effects of diurnal temperature range and drought on wheat yield in Spain

Cited by 41 publications

References 95 publications

Probabilistic modelling of the dependence between rainfed crops and drought hazard

Probabilistic modelling of the dependence between rainfed crops and drought hazard

Wheat yield in Spain and associated solar radiation patterns

Modeling Regional Effects of Climate Change on Soil Organic Carbon in Spain

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