09:45 〜 10:00
[ACG46-04] Long-term crop yield trends and their response to climate variability: A case study in India (1900-2020)
キーワード：Agricultural production、Historical crop data、Food security、Climate variability
Global food security, is one of essential components of the Sustainable Development Goals (SDGs). Agricultural production is easily affected by both human activities and climate change, which can in turn threaten food security (Liu et al., 2017; Karthikeyan et al., 2020). Understanding long-term crop yield trends is crucial for balancing global food supplies, ensuring economic stability, and maintaining environmental sustainability.
Large-scale interannual climate variability patterns, such as the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), are key drivers of global interannual climate variability (Casa et al., 2021). These patterns affect atmospheric circulation, surface temperature, and precipitation, which in turn affect crop yield trend (Anderson et al., 2023). A strong correlation has been observed between these climate variability patterns and crop production in regions such as Europe, South America, Asia, and the United States (Lizumi et al., 2014). The relatively higher predictability of climate variability compared to crop production offers an opportunity for developing effective agricultural risk management strategies.
India, relying heavily on agriculture for national economic and social stability, plays a significant role in global food markets and pricing. Moreover, the agricultural production in India is heavily dependent on monsoon rainfall, which is influenced by interannual climate variabilities such as the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) (Rajbanshi and Das, 2021; Mishra et al., 2022). This study therefore aims to: 1) analyze the long-term crop trends in India, and 2) investigate the relationships between climate oscillations and crop yield. This study will provide crucial support for global food security and provide deeper insights into interactions between agricultural production and climate change, further improving agricultural resilience and developing risk management strategies.
We collected the production, planting surface and yield data of Barley, Maize and Rice from 1900 to 2020 across various state of India, using book resources such as A&Y of Certain Principal Crops, Statistical Abstract, Area & Production of Principal Crops, Rice in India, etc. We removed outliers exceeding three standard deviations or five times the historical mean. The filtered dataset consists of 3,270 data points, including 1,644 for Rice, 817 for Barley, and 809 for Maize. To capture long-term yield trends and reduce extraneous influences, we employed Dynamic Linear Models (DLMs). Yield anomalies were then defined as the difference between observed yields and DLMs estimates. Finally, we examined how these anomalies relate to negative and positive phases of ENSO and IOD events.
Our results indicated that overall long-term yield trends were similar among Barley, Maize and Rice in India. Based on historical data from 1900 to 2020, we identified four phases: 1) 1900–1949: Low yields with negative growth. 2) 1950–1974: Rapid yield expansion 3) 1975–1999: Continued but slower growth 4) 2000-2020: High yield with steady growth. Regarding the crop distribution in India, Barley tended to have higher yields in the northwest, Maize in the southeast, and Rice in both the northwest and southeast. Over the past century, ENSO and IOD events have exhibited dynamic influences on major crops in India. El Niño had a positive effect on barley and maize growth, but later tended to be harmful. Conversely, La Niña, which used to negatively affect maize growth, then evolved to benefit it. Positive IOD often exerted a uniform effect across all three crops.
Large-scale interannual climate variability patterns, such as the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), are key drivers of global interannual climate variability (Casa et al., 2021). These patterns affect atmospheric circulation, surface temperature, and precipitation, which in turn affect crop yield trend (Anderson et al., 2023). A strong correlation has been observed between these climate variability patterns and crop production in regions such as Europe, South America, Asia, and the United States (Lizumi et al., 2014). The relatively higher predictability of climate variability compared to crop production offers an opportunity for developing effective agricultural risk management strategies.
India, relying heavily on agriculture for national economic and social stability, plays a significant role in global food markets and pricing. Moreover, the agricultural production in India is heavily dependent on monsoon rainfall, which is influenced by interannual climate variabilities such as the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) (Rajbanshi and Das, 2021; Mishra et al., 2022). This study therefore aims to: 1) analyze the long-term crop trends in India, and 2) investigate the relationships between climate oscillations and crop yield. This study will provide crucial support for global food security and provide deeper insights into interactions between agricultural production and climate change, further improving agricultural resilience and developing risk management strategies.
We collected the production, planting surface and yield data of Barley, Maize and Rice from 1900 to 2020 across various state of India, using book resources such as A&Y of Certain Principal Crops, Statistical Abstract, Area & Production of Principal Crops, Rice in India, etc. We removed outliers exceeding three standard deviations or five times the historical mean. The filtered dataset consists of 3,270 data points, including 1,644 for Rice, 817 for Barley, and 809 for Maize. To capture long-term yield trends and reduce extraneous influences, we employed Dynamic Linear Models (DLMs). Yield anomalies were then defined as the difference between observed yields and DLMs estimates. Finally, we examined how these anomalies relate to negative and positive phases of ENSO and IOD events.
Our results indicated that overall long-term yield trends were similar among Barley, Maize and Rice in India. Based on historical data from 1900 to 2020, we identified four phases: 1) 1900–1949: Low yields with negative growth. 2) 1950–1974: Rapid yield expansion 3) 1975–1999: Continued but slower growth 4) 2000-2020: High yield with steady growth. Regarding the crop distribution in India, Barley tended to have higher yields in the northwest, Maize in the southeast, and Rice in both the northwest and southeast. Over the past century, ENSO and IOD events have exhibited dynamic influences on major crops in India. El Niño had a positive effect on barley and maize growth, but later tended to be harmful. Conversely, La Niña, which used to negatively affect maize growth, then evolved to benefit it. Positive IOD often exerted a uniform effect across all three crops.