Dual Pathway Model of Responses Between Climate Change and Livestock Production

Abstract This chapter was aimed at evaluating the responses of livestock to fluctuations in climate and the debilitating effect of livestock production on the environment. Survey of livestock stakeholders (farmers, researchers, marketers, and traders) was carried out in Sahel, Sudan, Northern Guinea Savannah, Southern Guinea Savannah, and Derived Savannah zones of Nigeria. In total, 362 respondents were interviewed between April and June 2020. The distribution of the respondents was 22 in Sahel, 57 in Sudan, 61 in Northern Guinea Savannah, 80 in Southern Guinea Savannah, and 106 in Derived Savannah. The respondents were purposively interviewed based on their engagement in livestock production, research or trading activities. Thirty-eight years’ climate data from 1982 to 2019 were obtained from Nigerian Metrological Agency, Abuja. Ilela, Kiyawa, and Sabon Gari were chosen to represent Sahel, Sudan, and Northern Guinea Savannah zone of Nigeria, respectively. The data contained precipitation, relative humidity, and minimum and maximum temperature. The temperature humidity index (THI) was calculated using the formula: THI = 0.8*T + RH*(T-14.4) + 46.4, where T = ambient or dry-bulb temperature in °C and RH=relative humidity expressed as a proportion. Three Machine Learning model were built to predict the monthly minimum temperature, maximum temperature, and relative humidity respectively based on information from the previous 11 months. The methodology adopted is to treat each prediction task as a supervised learning problem. This involves transforming the time series data into a feature-target dataset using autoregressive (AR) technique. The major component of the activities of livestock that was known to cause injury to the environment as depicted in this chapter was the production of greenhouse gases. From the respondents in this chapter, some adaptive measures were stated as having controlling and mitigating effect at reducing the effect of activities of livestock on the climate and the environment. The environment and climate on the other side of the dual pathway is also known to induce stress on livestock. The concept of crop-livestock integration system is advocated in this chapter as beneficial to livestock and environment in the short and long run. Based on the predictive model developed for temperature and relative humidity in a sample location (Ilela) using Machine Learning in this chapter, there is need for development of a web or standalone application that will be useable by Nigerian farmers, meteorological agencies, and extension organizations as climate fluctuation early warning system. Development of this predictive model needs to be expanded and made functional.