Agricultural Drivers of Climate Change: Adaptation Mitigation Strategies

1429 Online ISSN: 3006-2047 Print ISSN: 3006-2039 Agricultural Drivers of Climate Change: Adaptation Mitigation Strategies Muhammad Saleem Institute of Agricultural Extension, Education and Rural Development, University of Agriculture, Faisalabad Dr Shahinshah khan Associate professor, Department of Agriculture Extension, Balochistan Agriculture College, Quetta Muhammad Adnan Aslam*(Corresponding Author) Institute of Agricultural Extension, Education and Rural Development, University of Agriculture, Faisalabad Email: [email protected] Abdul Ghaffar Research Officer, Directorate of Agriculture Research Zhob, Department of Agriculture Research, Balochistan Shah Alam Mandokhail Horticulturist, Deputy Director, Department of Agriculture Research, Musakhail, Balochistan Abstract Climate change has become an uphill task towards agricultural sustainability especially in developing countries where farmers are dependent on climate sensitive resources. This paper evaluates agricultural causes of climate change and looks at the factors that affect adaptation and mitigation methods used by farmers in the Muzaffargarh district, which is a big agricultural center in South Punjab in Pakistan. The district was selected purposely because of its variety of cropping systems and continuous interventions of governmental and non-governmental organizations that advance the idea of climate-resilient agriculture. Simple random sampling was used to select a sample of 120 farmers and data collected in form of a structured questionnaire by conducting face-to-face interview. Moderate climate change knowledge (M = 3.26), adaptation (M = 2.88), and mitigation practices (M = 2.67) were found, and the perceived climate change impact levels were relatively high (M = 3.43). The correlation analysis revealed a positive relationship between the knowledge and adaptation and mitigation behaviors, which is a good indication of the significance of awareness in determining climate-responsive actions. Regression analysis has shown that knowledge was the best predictor of adaptation (b = 0.36, p < .001) and mitigation (b = 0.31, p < .001). Perceived impact had a strong effect on adaptation (b 1430 Online ISSN: 3006-2047 Print ISSN: 3006-2039 = 0.21, p =.002) but not mitigation, which indicates that farmers adopt short-term coping mechanisms over long-term emission reduction. Ownership of livestock was found to have a marginal positive impact on adaptation and significant negative impact on mitigation indicating the complexity of livestock-related climate interactions. The size of farms did not make a significant difference so that behavioral responses within the groups of different landholdings were similar. Unreasonable fertilizers application was found to be negatively correlated to mitigation (p =.010), which underscores the unsustainable use of nutrients as a principal cause of agricultural emissions. In general, the results underline the primary position to consider farmer knowledge, perception of risk, and resource management in the formation of climate change adaptation and mitigation behavior. The paper highlights the necessity of reinforced extension services, specialized climate-smart agricultural training, and reinforced assistance to livestock-based farmers in order to develop sustainable agricultural systems that can decrease the effects of climate and increase the resilience. Key Words: Climate, Adaptation, Mitigation, Agriculture, Sustainability. Introduction Agriculture contributes significantly to climate change in the world by the emission of greenhouse gases (GHGs) including carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Approximately 10% of worldwide GHG emissions are attributed to the sector, and they are mainly achieved through livestock production, use of fertilizers, rice production, and land-use modifications (Moore & Bruggen, 2010; Wojcik-Gront, 2020). The emissions are particularly apparent in the less developed areas, including sub-Saharan Africa, where the effects of increased temperatures and the change in the rainfall patterns complicate the problems of farmers (Omotoso and Omotayo, 2023). Methane is a major agricultural pollutant and the sector accounts to approximately one-third of all methane emissions in the globe with livestock and rice farming being the primary contributors (Balogh, 2020). Crop residue burning and the growing adoption of artificial fertilizers only raise the levels of emissions and contribute to climate change (Balogh, 2020; Obiora and Madukwe, 2013). Although the overall emissions of agriculture in the world are growing, in the Annex I countries, there has been a slow decrease in emissions, particularly in the fields of enteric fermentation and manure management (Wojcik-Gront, 2020). Climate change is also affected differently by several major crops. An example is rice, which is one of the most emitting crops, produces large amounts of methane, and occupies almost 48 per cent of the GHG emissions of the croplands in certain areas (Carlson et al., 2017). Maize, which has high nitrogen requirement, is one of the crops that contribute significantly to the production of N2O, and soybean is a more viable crop option because of its less nitrogen demand and effects on the environment (Ray et al., 2019; Guo et al., 2024). Conversely, millets are known to have a low resource intensity and lower GHG, and thus, they can be used as a climate-smart alternative to sustainable agriculture (Wang et al., 2018). 1431 Online ISSN: 3006-2047 Print ISSN: 3006-2039 The increasing awareness of the climate effect of agriculture has led to the stress on adaptation and mitigation measures. These are climate-sensitive farming methods, better livestock husbandry, low intake of meat, optimization of fertilizers and adopting of sustainable crop alternatives. These measures contribute to the resilience to severe weather, better crop productivity, and the prioritization of economic, social, and environmental aspects (Nunes, 2023; Shuvar et al., 2024). Methodology The Muzaffargarh district that has a population of 3,528,567, is an important agricultural area in Punjab. Geographically, it falls between the two main rivers of the subcontinent the Chenab and the Indus. The area is famous in terms of diverse cropping. The main crops are wheat, sugarcane and cotton, and rice, jawar, bajra, moong, mash, masoor, groundnut, maize and different oilseeds (rapeseed, sunflower etc.) are grown on a smaller scale. The key fruit crops are mangoes, dates, citrus, and pomegranate; jaman, pears, phalsa, and bananas are also of small scale. The research was carried out in the Muzaffargarh district, which was chosen out of the purposive selection based on its central position in agricultural production in South Punjab and its suitability to the aim of the research. The district has been known as having ageing farmers with a high percentage of people being illiterate or having low education levels which affect the agricultural decisions and adoptions of modern practices. Furthermore, a number of non-governmental and state agencies are also operating in the district to foster the welfare of farmers, their awareness, and the use of better and more climate-resistant agricultural practices. This made the district a suitable location of the current study. A sample of 120 farmers was selected in the district selected out of a simple random sample of 120 farmers. The respondents were contacted on a one-on-one basis to ascertain the right and valid data gathering. A structured questionnaire was used as a data collection tool and was conducted by face-to-face interviews. Results and Discussion Table 1. Descriptive Statistics of Demographic Attributes and Climate-Related Scores (Awareness, Resilience, Abatement, and Perceived Risk) Variable Mean SD Min Max Age (years) 44.18 13.75 20 69 Farm Size (ha) 10.92 7.34 1.2 38.6 Fertilizer (kg/ha) 137.44 63.22 14.8 286.1 Income (USD) 5,646 2,948 1,506 17,586 Knowledge Score 3.26 0.59 1.67 5.00 Adaptation Score 2.88 0.65 1.50 4.75 Mitigation Score 2.67 0.63 1.25 4.75 Perceived Impact 3.43 0.90 1.00 5.00 1432 Online ISSN: 3006-2047 Print ISSN: 3006-2039 The descriptive statistics of the respondents and key study variables are presented in Table 1. The average age of the farmers was 44.18 years (SD = 13.75), indicating that most participants were middle-aged and actively engaged in farming activities. The mean farm size was 10.92 hectares (SD = 7.34), with a range of 1.2 to 38.6 hectares, reflecting variability in landholding patterns. Fertilizer use among farmers averaged 137.44 kg/ha (SD = 63.22), while annual income varied widely, with a mean of USD 5,646 (SD = 2,948), suggesting notable differences in economic capacity and resource availability among the respondents. Regarding climate change-related variables, farmers exhibited a moderate level of knowledge with a mean score of 3.26 (SD = 0.59). The average adaptation score was 2.88 (SD = 0.65), indicating that while some adaptive measures were implemented, overall adoption of climate-resilient practices was limited. Similarly, mitigation practices were less prevalent, with a mean score of 2.67 (SD = 0.63). Farmers’ perceived impact of climate change was relatively high, with a mean score of 3.43 (SD = 0.90), suggesting that respondents are generally aware of the consequences of climate change on their agricultural activities. Table 2 Correlation between different variables Variable Know Adapt Mitig Impact Farm Size Fertilizer Income Knowledge Score 1 0.41 0.38 0.22 0.05 −0.17 0.09 Adaptation Score 0.41 1 0.47 0.33 0.12 −0.09 0.14 Mitigation Score 0.38 0.47 1 0.29 0.02 −0.11 0.07 Perceived Impact 0.22 0.33 0.29 1 −0.03 0.00 −0.05 Farm Size (ha) 0.05 0.12 0.02 −0.03 1 0.16 0.53 Fertilizer (kg/ha) −0.17 −0.09 −0.11 0.00 0.16 1 0.27 Income (USD) 0.09 0.14 0.07 −0.05 0.53 0.27 1 Table 2 shows the correlation analysis between the important variables of the studying. Adaptation (r = 0.41) and mitigation scores (r = 0.38) had a significant positive association with knowledge score, which proved that farmers possessing better knowledge about climate change have higher chances of using adaptation and mitigation strategies. Adaptation (r = 0.33) and mitigation scores (r = 0.29) also showed a positive correlation, with perceived impact of climate change which indicates that the more farmers perceive the effects of climate change, the more responsive steps they will take. Farm size was positively and moderately correlated with income (r = 0.53) which indicated that bigger farm is associated with higher income. There was a negative correlation between knowledge ( = 0.17) and adaptation ( = 0.09) and mitigation scores ( = 0.11) and the use of fertilizers which could be attributed to excessive dependence on traditional methods and the need to adopt climate-compatible measures. The rest of the correlations between variables were most often weak, which 1433 Online ISSN: 3006-2047 Print ISSN: 3006-2039 means that besides the resources endowment (farm size and farm income) knowledge and perception are more important in determining the adaptation and mitigation behavior of the farmers. Table 3 Regression analysis of Human and farm-level drivers which determine the adaptation and mitigation practices3 Predictor β (Beta) SE t p-value Knowledge Score 0.36 0.08 4.52 < .001 Perceived Impact 0.21 0.06 3.22 .002 Has Livestock 0.14 0.07 1.96 .052 Farm Size (ha) 0.04 0.03 1.23 .221 Fertilizer (kg/ha) −0.01 0.00 −1.78 .077 The regression findings offer a valuable understanding of the human and farm-level drivers which determine the adaptation and mitigation practices, which are major factors in dealing with the drivers of climate change, in an agricultural system. Knowledge Score is the most powerful predictor (b = 0.36, p <.001). This implies that climate change, sustainable practices and environmental impacts are the factors that farmers with more knowledge on them are more likely to engage in adoption of adaptation and mitigation strategies. Capacity building is therefore founded on knowledge that plays a pivotal role in reduction of emission and resilience enhancement as a result of agriculture. Similarly, Perceived Impact shows a high positive effect (b = 0.21, p =.002) to suggest that the attitude that farmers hold regarding the severity and relevance of climate change has a huger influence on their willingness to perform. When farmers know how climate change will affect their productivity, soil health and long-term sustainability, they will be more willing to adopt climate-smart action such as resource-efficient irrigation and soil conservation and integrated pest management. The Has Livestock (b = 0.14, p =.052) variable is marginally significant, indicating that the owners of the livestock were a little more apt to be more concerned with adaptation and mitigation practices. This may be in the form of the increased exposure of the risk of the climate (such as lack of fodder, heat stress and epidemics) or increased dependence on the sustainable manner of resources management. The livestock production has also contributed to agricultural emissions, therefore, improved livestock management practice by this group would generate massive climate benefits. The inconsequential variables like Farm Size (b = 0.04, p =.221) imply that big landholders are not the only ones who are sensitive to climateboth small and large farmers behave similarly as far as adaptation and mitigation are concerned. This is a very important finding because it highlights the reality that climate-smart agriculture interventions should be all-inclusive and should be applicable in all sizes of farms. 1434 Online ISSN: 3006-2047 Print ISSN: 3006-2039 Lastly, the Fertilizer Use correlates with the negative relation with a slight and nonsignificant correlation (b = -0.01, p =.077). This is not a significant difference, but the negative direction may indicate the ineffectiveness or excessive use of fertilizers which is the primary source of nitrous oxide emissions, one of the primary causes of climate change. This observation highlights the need to have more effective nutrient management systems such as precision agriculture and balanced fertilization to reduce emissions without reducing productivity. Table 4 Descriptive Statistics of Key Constructs among Farmers Variable Mean SD Min Max Knowledge Score 3.26 0.59 1.67 5.00 Adaptation Score 2.88 0.65 1.50 4.75 Mitigation Score 2.67 0.63 1.25 4.75 Perceived Impact of Climate Change 3.43 0.90 1.00 5.00 The descriptive findings give the general picture of the level of knowledge, adaptation and mitigation behaviors and the perceptions of the farmers towards climate change. The Knowledge Score has a average of 3.26 (SD = 0.59), which is a moderately high level of awareness of the majority of the respondents about climate change and the issues associated with agriculture. Nevertheless, the variation is quite large (1.67-5.00) indicating variability, some of the farmers possess very thin knowledge. This underscores the importance of tailored training and sensitization activities to make sure everyone among the farmers has been well informed to practice climate-smart activities. The mean score of the Adaptation Score is 2.88 (SD = 0.65) indicating moderate involvement in the adaptation strategies. That means that farmers are not completely eliminating adaptive practices, i.e. conservation of water and the crop diversification, or the use of stress-tolerant varieties, yet there is still a way to improve. The difference in scores suggests the adoption will be dependent on the access to resources, knowledge, and perceived vulnerability. In a similar manner, the Mitigation Score (M = 2.67, SD = 0.63) shows the rather low level of participation in mitigation activities as opposed to adaptation. This implies that farmers are more concerned with the urgent climate-based issues as opposed to the long-term measures to reduce emissions. Such practices as effective use of the fertilizers, less tillage, and better animal management might not be as broadly adopted yet, perhaps because of cost or lack of awareness, or perhaps technologically. The peer-reviewed article Perceived Impact of Climate Change presents a relative high mean of 3.43 (SD = 0.90) which illustrates that the majority of the respondents think that climate change is already impacting their farming practices. The wide range (1.00-5.00) indicates that whereas some farmers are quite aware of the effects, there are still those who continue to undervalue or are not fully aware of the severity of the 1435 Online ISSN: 3006-2047 Print ISSN: 3006-2039 threats of climate. Perception is a vital motivation in an action; therefore, the augmentation of awareness can further promote adaptation and mitigation actions. Table 5 Determinants of Climate Change Adaptation and Mitigation Strategie The regression of the adaptation and mitigation behaviors are useful in understanding the motivation of climate-smart farming behaviors. The adaptation model has a statistical significance of 28% in explaining the variation in adaptation behavior and this is statistically significant meaning that the predictors included have a significant role to play in the adaptive behavior of farmers. Knowledge Score comes out as the best positive predictor of adaptation. Farmers that have better understanding of climate change have a high probability of embracing adaptive strategies like better water management schemes, crop diversification, and stronger varieties. This underscores the importance of awareness, education as well as technical training in enhancing agricultural resilience. The other predictor is Perceived Impact, which implies that farmers who perceive themselves more adversely by climate change have more incentive to implement adaptation. The Has Livestock variable presents a marginally significant effect, which means that the owners of livestock might make a little more adaptive actions because the livestock systems are exposed to feed shortages, heat stress, and disease outbreaks. On the other hand, Farm Size does not make any significant impact showing that adaptation behavior is independent of landholding. The Fertilizer Use is slightly negative, which means that farmers who use fertilizers intensively might not be willing to follow the climate-smart practices. The explanatory power of the mitigation model is also very high and as such, 23 percent of the variation in mitigation behavior is explained by the model. Like the Predictor Adaptation Score Mitigation Score β (SE) p-value β (SE) p-value Knowledge Score 0.36 (0.08) < .001 0.31 (0.07) < .001 Perceived Impact 0.21 (0.06) .002 0.08 (0.05) .118 Has Livestock (Yes=1) 0.14 (0.07) .052 -0.18 (0.06) .003 Farm Size (ha) 0.04 (0.03) .221 -0.02 (0.03) .512 Fertilizer Use (kg/ha) -0.01 (0.00) .077 -0.02 (0.00) .010 Constant 1.12 (0.25) < .001 1.45 (0.23) < .001 Model Fit R² = 0.28, Adj. R² = 0.25, F = 8.91, p < .001 R² = 0.23, Adj. R² = 0.20, F = 6.87, p < .001 1436 Online ISSN: 3006-2047 Print ISSN: 3006-2039 adaptation model, the knowledge is still a powerful and important predictor but it states that knowledgeable farmers are in a better position to implement long-term emission-reducing activities, including efficient application of fertilizers, less tillage, and livestock minimization. Nonetheless, Perceived Impact does not have a significant effect on mitigation behavior, indicating that the awareness of the farmers about climate effects can drive them to putting in place preventive actions instead of taking long-term measures on greenhouse gas reduction. It is important to note that the ownership of livestock significantly impacts negatively mitigation behaviors meaning that livestock producers are less likely to undertake activities that reduce the emission. This shows it is a significant discovery since livestock systems have been cited as a major cause of methane emission, and climate-smart livestock technologies can be achieved by applying specific interventions. Similar to the adaptation model, the Size of Farm is not significant, and it proves that mitigation behaviors are not conditioned by the size of landholding. Fertilizer Use has a significant negative impact and this implies that the more the farmers that use more fertilizer, the less likely they are to implement mitigation practices, possibly because of reliance on production systems based on input intensive production. Table 6: Correlation Matrix of Key Variables Variable Know Adapt Mitig Impact Farm Size Fertilizer Income Knowledge Score 1 0.41 0.38 0.22 0.05 −0.17 0.09 Adaptation Score 0.41 1 0.47 0.33 0.12 −0.09 0.14 Mitigation Score 0.38 0.47 1 0.29 0.02 −0.11 0.07 Perceived Impact 0.22 0.33 0.29 1 −0.03 0.00 −0.05 Farm Size (ha) 0.05 0.12 0.02 −0.03 1 0.16 0.53 Fertilizer (kg/ha) −0.17 −0.09 −0.11 0.00 0.16 1 0.27 Income (USD) 0.09 0.14 0.07 −0.05 0.53 0.27 1 The correlation matrix offers valuable information on the interaction between various variables in the environment of climate change adaptations and mitigations among the farmers. Knowledge Score forms positive correlations with both Adaptation (r = 0.41) and Mitigation (r = 0.38), which are moderate and positive; hence, farmers who possess higher climate-related knowledge have a higher chance of engaging in adaptation and mitigation practices. This supports the role of awareness and training activities as the basic engines of climate-smart agriculture. There is also a weak positive correlation between Knowledge and Perceived Impact (r = 0.22), where more informed farmers might have a better knowledge of the risks of climate. 1437 Online ISSN: 3006-2047 Print ISSN: 3006-2039 Adaptation Score is positively correlated with Mitigation Score (r = 0.47), which means that farmers that switch to adaptive (e.g., water-saving) strategies are also likely to adopt mitigation (e.g., reducing the use of fertilizers, managing livestock better, etc.). This complementary relationship implies that improving adaptation might indirectly increase the process of mitigation. The relationships between Perceived Impact and the behavioral outcomes of the Adaptation (r = 0.33) and Mitigation (r = 0.29) variables show that the more climate change influences farmers, the more they act. This highlights the importance of risk perception in developing climate responsive behavior. There are different patterns of socioeconomic variables. Farm Size Farm Size has low or insignificant correlations with Knowledge, Adaptation, Mitigation, and Impact, suggesting that the size of landholding does not play a major role in climate-related behavior of the farmers. Farm Size is, however, strongly related with Income (r = 0.53) which is not surprising because larger farms tend to bring more income. Farm Size is also positively, but insignificantly related to Fertilizer Use (r = 0.16), which indicates that, larger farms are more likely to use more fertilizer which could be contributing to the higher level of emissions. There are weak negative correlations between Fertilizer Use and Knowledge, Adaptation and Mitigation. Though insignificant, small negative relationships suggest that more intensive use of fertilizers can be linked with a reduced practice of climatesmart practices, which could be explained by the traditional methods of farming based on inputs. There is a moderate correlation between Fertilizer use and Income (r = 0.27), which demonstrates the purchasing power and the ability to afford it by wealthier farmers. Table 7 Predictors of Farmers’ Adaptation Measures: Regression Output Predictor Unstandardized B Standardized Beta (β) SE t p-value (Constant) 1.12 0.25 4.48 < .001 Knowledge Score 0.40 0.36 0.08 4.52 < .001 Perceived Impact 0.15 0.21 0.06 3.22 .002 Has Livestock (Yes=1) 0.14 0.14 0.07 1.96 .052 Farm Size (ha) 0.01 0.04 0.03 1.23 .221 Fertilizer Use (kg/ha) -0.001 -0.01 0.00 -1.78 .077 Model Summary R 0.53 R² 0.28 Adjusted R² 0.25 F-statistic 8.91 p-value (Model) < .001 The outcome of the multiple linear regression analysis shows that the model has a significant amount of variation in the dependent variable with an overall R2 of 0.28.