中国农业科学

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最新录用:江苏奶牛热应激风险区划及其受气候变化的影响

任义方1,杨章平2*零丰华3,肖良文4
  

  1. 1江苏省气候中心,南京  2100082扬州大学动物科学与技术学院,江苏扬州 2250093南京信息工程大学大气科学学院,南京 2100444江苏桢源应用气象研究院有限公司,南京 211100
  • 发布日期:2022-10-09

Risk Zoning of Heat Stress Risk Zoning of Dairy Cows in Jiangsu Province and its Characteristics Affected by Climate Change

REN YiFang1, YANG ZhangPing2*, LING FengHua3, XIAO LiangWen4 #br#   

  1. 1Jiangsu Meteorological Service Center, Nanjing 210008; 2 College of animal science and technology, Yangzhou University, Yangzhou 225009 Jiangsu; 3 College of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210008; 4 Jiangsu Zhenyuan Applied Meteorology Research Institute Co., Ltd, Nanjing 211100
  • Online:2022-10-09

摘要: 【目的】掌握气候变化背景下奶牛热应激发生规律,可为畜牧业优化布局、牧场智能管控、选址改造、效益提升等方面提供参考,有助于优化牧场生产管理,促进奶牛生态健康养殖。【方法】以江苏省为例,利用1980—2020年全球大气再分析资料ERA5数据集,基于表征奶牛热应激程度的温湿指数(THI)构建风险度指数(RI),选择k均值聚类算法实现奶牛热应激风险区划,结合热应激发生强度、频率、起止时间、持续日数的特征开展区域评估;求算气候倾向率,分析不同风险区内奶牛热应激发生特征的变化趋势;基于累积温湿指数(CTHI),利用Mann-Kendall检验,判定不同风险区气候突变点,进而从逐日和逐小时两个时间尺度,分析气候变化对不同风险区内奶牛热应激发生特征的影响。【结果】江苏奶牛热应激风险呈现西南高东北低的特征低风险区主要包括淮北和江淮之间中东部地区,区域温湿指数均值73.63,以轻度热应激发生为主;高风险区主要包括沿江苏南和江淮之间西部地区,区域温湿指数均值75.12,轻、中度热应激发生频次相当。低、高风险区域中热应激开始和结束时间均呈现提前和推后趋势,持续日数呈延长趋势(4.0 d/10a4.2 d/10a);温湿指数值增加(0.2/10a);累积温湿指数分别增加301.2/10a256.1/10a轻度热应激发生频次均呈双峰型,主要发生在6月上旬至7月中旬和8月上旬至9月中旬;中度热应激发生频次均呈单峰型,主要发生在7月中旬至8月中旬日热应激强度变化基本呈现正弦分布形态,高发时段集中在11:00-17:00受气候变化影响,江苏全省奶牛热应激呈现明显增强趋势,至2010年达到小高峰,有一回落后呈稳步上升趋势,且超过显著性水平0.05临界线。低、高风险区域内热应激高影响时段延长、发生频率增加、覆盖度提高且出现时间前移;日热应激高发时段开始时间提前1h左右,高风险区热应激发生强度基本接近中等【结论】基于THIRICTHI可以实现奶牛热应激风险区划评估及气候影响分析,确定奶牛热应激的高发区域和关键防控时段,把握其气候变化趋势。随气候变化,江苏奶牛热应激发生呈现“趋早、趋强、趋长、趋多”的特征,需积极应对。


关键词: 江苏, 奶牛, 热应激, 风险区划评估, 气候变化

Abstract: 【ObjectiveIn order to optimize the production management of pasture and promote ecological and healthy breeding level of dairy cows, mastering the occurrence law of cow heat stress under the background of climate change could provide reference for optimizing the layout of animal husbandry, intelligent management and control of pasture, site selection and transformation, benefit improvement and so on. MethodTaking Jiangsu Province as an example, using the Era5 data set of global atmospheric reanalysis data from 1980 to 2020, the risk index (RI) was constructed based on the temperature humidity index (THI), which represents the degree of cow heat stress. The K-means clustering algorithm was selected to realize the risk zoning of heat stress of dairy cows, and the regional evaluation was carried out in combination with the characteristics of occurrence intensity, frequency, start and end time, as well as duration of heat stress. The climate tendency rate was calculated to analyze the change trend of the characteristics of cow heat stress in different risk areas. Based on the cumulative temperature humidity index (CTHI), Mann-Kendall test was used to determine the climate mutation points in different risk areas. Furthermore, the impacts of climate change on the occurrence characteristics of cow heat stress in different risk areas were analyzed from the daily and hourly time scales, respectively. ResultThe study showed that the risk of heat stress of dairy cows in Jiangsu Province presented the distribution features of "high in the southwest and low in the northeast". The low-risk areas mainly included Huaibei and the middle-eastern area of Yangtze river and Huai river valley. The regional averaged value of THI was 73.63, and mild heat stress mainly occurred. The high-risk areas mainly include the areas along the southern Jiangsu and the west area of Yangtze river and Huai river valley. The regional averaged value of THI was 75.12, and the occurrence frequency of mild and moderate thermal stress was nearly the same. In the low-risk and high-risk areas, the start and end time of heat stress showed an advanced and delay trend, and the duration days showed an extended trend of 4.0 d/(10a) and 4.2 d/(10a) respectively, the values of THI all showed an increasing trend of 0.2/(10a),while the value of CTHI showed an increasing trend of 301.2/(10a) and 256.1/(10a) respectively. The frequencies of mild thermal stress were bimodal, mainly occurred from the early-June to the mid-July, and from the early-August to the middle-September, while the frequency of moderate thermal stress was unimodal, mainly occurred from mid-July to mid-August. The change of daily heat stress intensity basically presented a distribution of "sinusoidal", and the high incidence period was concentrated in 11:00-17:00. Affected by climate change, the heat stress of dairy cows in Jiangsu Province showed an obvious increasing trend, reaching a small peak in 2010. Then after a decline, it showed a steady strengthening trend, which exceeded the threshold of 0.05 significance level. In low and high risk areas, the highly impacted periods of cow heat stress were prolonged, the occurrence frequency increased, the coverage increased and the starting-time moved forward. The daily beginning time of the high incidence period of cow heat stress moved forward for about 1 hour, and the intensity of heat stress in high-risk areas was basically increased close to the medium level. ConclusionBased on THI, RI and CTHI, the risk zoning assessment and climate impact analysis of cow heat stress could be realized, the high-risk areas, and key prevention and control periods of cow heat stress could be determined, and the climate change trend could be grasped. With the climate change, the heat stress of dairy cows in Jiangsu Province shows the characteristics of "earlier, stronger, longer and more", which should be actively dealt with.


Key words: Jiangsu, cow, heat stress, risk zoning assessment, climate change