Relationship Between Weather Conditions and Climate Indices with Rainfed Crop Yield

Soureshjani, Hedayatollah Karimzadeh

doi:10.1007/s42106-021-00160-6

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…The few previous analyses using AI to explain yields are limited in geographical and climatic range and are all based on linear relationships. They showed yield increased with AI, at the annual scale in wheat and barley in Iran (Bannayan et al, 2010; Karimzadeh Soureshjani, 2021), during the growing season in soybean and wheat, and within shorter periods in maize and rice in north‐eastern China (Yin et al, 2016). Despite not considering these more complex dependences, the ratio of monthly precipitation to temperature—somewhat proportional to AI—explained 34% to 78% of maize and sorghum yield variation depending on month and location across Botswana (Byakatonda et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…in wheat and barley in Iran (Bannayan et al, 2010;Karimzadeh Soureshjani, 2021), during the growing season in soybean and wheat, and within shorter periods in maize and rice in north-eastern China (Yin et al, 2016). Despite not considering these more complex dependences, the ratio of monthly precipitation to temperature-somewhat proportional to AI-explained 34% to 78% of maize and sorghum yield variation depending on month and location across Botswana Another possible explanation is that crops are generally more anisohydric than other species (Konings & Gentine, 2016), that is, they…”

Section: Discussionmentioning

confidence: 99%

“…In spite of this potential, AI or some of its modifications have so far mostly been used for crop suitability delineation (van Wart et al, 2013). The few predictions of yield based on AI are limited in geographical scales and ranges of climatic conditions (Bannayan et al, 2010; Karimzadeh Soureshjani, 2021; Yin et al, 2016). As such, the ability of AI to explain yield variability compared with precipitation, temperature and their interaction remains unknown over a wide range of conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Based on these relationships, we can expect that crop yields are highest at intermediate AI, in line with the globally emerging mesic maximum (Good et al, 2017). However, the few attempts made to explain yields with AI assumed a linear dependence (Bannayan et al, 2010; Karimzadeh Soureshjani, 2021; Yin et al, 2016). Tests for an intermediate AI that maximizes yields are lacking.…”

Section: Introductionmentioning

confidence: 99%

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Coordinated evaporative demand and precipitation maximize rainfed maize and soybean crop yields in the USA

2022

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To understand how climate change affects crop yields, we need to identify the climatic indices that best predict yields. Grain yields are most often predicted using precipitation and temperature in statistical models, assuming linear dependences.However, soil water availability is more influential for plant growth than precipitation and temperature, and there is ecophysiological evidence of intermediate yield maximizing conditions. Using rainfed maize and soybean yields for 1970-2010 across the USA, we tested whether the aridity index, that is, the ratio of precipitation and potential evapotranspiration seasonal totals and a proxy of soil water availability, better predicts yield than growing season precipitation total, average temperature and their interaction. We also tested for non-monotonic responses allowing for intermediate yield-maximizing conditions. The aridity index alone explained 77% and 72% of maize and soybean yield variability, compared with 78% and 73% explained by temperature, precipitation and their interaction. Yield responses were non-monotonic, with yields maximized at intermediate precipitation and temperature as well as at intermediate aridity index of 0.79 for maize and 0.98 for soybean. The yield maximizing precipitation also increased with growing season average temperature, faster in maize than soybean. The intermediate yield maximizing conditions show that rainfed maize and soybean yields could both increase and decrease depending on whether climatic conditions come closer to or deviate from the yield maximizing conditions in the future. In most counties, during 1970-2010, the precipitation and aridity index were lower and temperature higher compared with those maximizing yields, suggesting that climate change will reduce yields.

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