江苏奶牛热应激风险区划及其受气候变化的影响

doi:10.3864/j.issn.0578-1752.2022.22.015

摘要/Abstract

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，区域温湿指数均值为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，区域温湿指数均值为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/10a、4.2 d/10a)；温湿指数值增加（0.2/10a)；累积温湿指数分别增加301.2/10a和256.1/10a；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1 h左右，高风险区热应激发生强度基本接近中等。【结论】 基于THI、RI和CTHI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: 江苏省, 奶牛, 热应激, 风险区划评估, 气候变化

Abstract:

【Objective】 The aim of this study was to master the occurrence law of cow heat stress under the background of climate change to optimize the production management of pasture and to promote ecological and healthy breeding level of dairy cows, which could provide the reference for optimizing the layout of animal husbandry, intelligent management and control of pasture, site selection and transformation, benefit improvement and so on. 【Method】Taking Jiangsu Province as an example, the risk index (RI) was constructed based on the temperature humidity index (THI) by using the Era5 data set of global atmospheric reanalysis data from 1980 to 2020, which represented 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 the different risk areas. Based on the cumulative temperature humidity index (CTHI), Mann-Kendall test was used to determine the climate mutation points in the 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. 【Result】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 the mild heat stress mainly occurred. The high-risk areas mainly included 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, which 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, which 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 the 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. 【Conclusion】 Based on THI, RI and CTHI, the risk zoning assessment and climate impact analysis of cow heat stress could be realized, the high-risk areas as well as 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 showed the characteristics of "earlier, stronger, longer and more", which should be actively dealt with.

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

任义方,杨章平,零丰华,肖良文. 江苏奶牛热应激风险区划及其受气候变化的影响[J]. 中国农业科学, 2022, 55(22): 4513-4525.

REN Yifang,YANG ZhangPing,LING Fenghua,XIAO LiangWen. Risk Zoning of Heat Stress Risk Zoning of Dairy Cows in Jiangsu Province and Its Characteristics Affected by Climate Change[J]. Scientia Agricultura Sinica, 2022, 55(22): 4513-4525.

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	轻Slight	中Moderate	重Severe
热应激等级 Grade of heat stress (k)	1	2	3
温湿指数 Temperature humidity index (THI)	72≤THI≤79	79<THI≤88	THI>88

区域 Area	风险度 Risk index	温湿指数多年均值 Multi-year average value of temperature humidity index	轻度热应激发生频率 Occurrence frequency of slight heat stress	中度热应激发生频率 Occurrence frequency of moderate heat stress	开始时间 Begin time	结束时间 End time	持续时间 Duration (d)
低风险区Low risk	47.95	73.63	0.41	0.20	6月11日	9月16日	97
高风险区High risk	54.42	75.12	0.38	0.30	6月8日	9月16日	100

		低风险区Low-risk areas		高风险区High-risk areas
		气候突变前 Before climate mutation	气候突变后 After climate mutation	气候突变前 Before climate mutation	气候突变后 After climate mutation
Feq_shs第一高峰期 First peak period of Feq_shs	起止日期Start-end dates	6/16-7/19	6/6-7/17	6/8-7/13	6/6-7/16
	覆盖度Coverage (%)	25.5	29.8	24.8	27.6
	均值Average	0.62	0.63	0.52	0.56
Feq_shs第二高峰期 Second peak period of Feq_shs	起止日期Start-end dates	8/4-9/8	8/15-9/10	8/12-9/13	8/20-9/18
	覆盖度Coverage (%)	24.1	18.4	21.9	0.20
	均值Average	0.61	0.54	0.58	0.58
Feq_{_mhs}高峰期 Peak period of Feq_mhs	起止日期Start-end dates	7/23-8/3	7/18-8/13	7/14-8/10	7/10-8/19
	覆盖度Coverage (%)	5.7	16.0	72	77.3
	均值Average	0.65	0.67	0.72	0.8
THI高峰时段 Peak period of THI	起止日期Start-end dates	11:00-17:00	10:00-17:00	11:00-17:00	10:00-17:00
THI高峰时段 Peak period of THI	均值Average	76.5	77.0	77.0	77.7