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<p>The assessment of agro-climatic resources requires that observation data series be free of urbanization bias. However, currently, there is no research on agricultural heat resources based on air temperature data adjusted for urbanization bias. Taking Jixian County, a typical agricultural area in Heilongjiang Province, China, as an example, we assess the agricultural heat resources using temperature data with urbanization-bias corrected. Daily mean air temperature data from the national station, regional stations and farm stations were used. Two farm stations were used as references to correct the urbanization bias of daily mean temperature and ≥ 10&#xa0;°C accumulated temperature at Jixian Station, and the accumulated temperature zones in the county were re-evaluated. From 1961 to 2022, both the annual mean temperature and ≥ 10&#xa0;°C accumulated temperature of Jixian Station exhibited a significant upward trend. The change rates were 0.42&#xa0;°C/decade for the annual mean temperature and 81.9&#xa0;°C/decade for the ≥ 10&#xa0;°C accumulated temperature. The contributions of urbanization to the warming trends both reached 18%. After eliminating the urbanization bias, average value of the ≥ 10&#xa0;°C accumulated temperature in the county from 1991 to 2022 was 2792.7&#xa0;°C, which was 77.8&#xa0;°C lower than that calculated solely from quality-controlled and homogenized data. The distribution of ≥ 10&#xa0;°C accumulated temperature zones from the new assessment changed notably. Most areas fell into the second accumulated temperature zone (secondary warm), and the first accumulated temperature zone (warmest) originally obtained based on the data without urbanization-bias correction disappeared. We show that using uncorrected data for calculating ≥ 10&#xa0;°C accumulated temperature and dividing accumulated temperature zone will significantly overestimate the agricultural heat resources of a region and pose a high risk of out-of-zone planting to agricultural production.</p>

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Assessment of agricultural heat resources based on urbanization-bias-corrected data: case of a county in Heilongjiang, China

  • 期刊论文

  • 发表日期：

    2025年9月30日

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  • LI CHEN,
  • GUOYU REN,
  • CHANGMING ZHOU,
  • LEI WEI,
  • YABIN LI,
  • HONGYAN SHI,
  • SONG WU

摘要

The assessment of agro-climatic resources requires that observation data series be free of urbanization bias. However, currently, there is no research on agricultural heat resources based on air temperature data adjusted for urbanization bias. Taking Jixian County, a typical agricultural area in Heilongjiang Province, China, as an example, we assess the agricultural heat resources using temperature data with urbanization-bias corrected. Daily mean air temperature data from the national station, regional stations and farm stations were used. Two farm stations were used as references to correct the urbanization bias of daily mean temperature and ≥ 10 °C accumulated temperature at Jixian Station, and the accumulated temperature zones in the county were re-evaluated. From 1961 to 2022, both the annual mean temperature and ≥ 10 °C accumulated temperature of Jixian Station exhibited a significant upward trend. The change rates were 0.42 °C/decade for the annual mean temperature and 81.9 °C/decade for the ≥ 10 °C accumulated temperature. The contributions of urbanization to the warming trends both reached 18%. After eliminating the urbanization bias, average value of the ≥ 10 °C accumulated temperature in the county from 1991 to 2022 was 2792.7 °C, which was 77.8 °C lower than that calculated solely from quality-controlled and homogenized data. The distribution of ≥ 10 °C accumulated temperature zones from the new assessment changed notably. Most areas fell into the second accumulated temperature zone (secondary warm), and the first accumulated temperature zone (warmest) originally obtained based on the data without urbanization-bias correction disappeared. We show that using uncorrected data for calculating ≥ 10 °C accumulated temperature and dividing accumulated temperature zone will significantly overestimate the agricultural heat resources of a region and pose a high risk of out-of-zone planting to agricultural production.