South Kivu province is an agropastoral area where small-scale agriculture predominates. Due to its location and potential, this area would contribute to providing food for the population of South Kivu and neighboring provinces and countries. However, despite its potential, South Kivu remains dependent on its neighbors for its food security. This dependency is due to several factors, including the security situation, which has deteriorated in rural areas with the presence of Congolese and foreign armed groups, the deterioration of basic infrastructure, and the lack of funding for the agricultural sector.
This research analyzes the economic performance of small-scale farming in the mountainous region of South Kivu in the Democratic Republic of Congo and its determinants, and ultimately the differentiating factors of the farms surveyed. The value-added distribution approach and the calculation of margins were used to analyze economic performance indicators, while multiple linear regression was used for an overall analysis of the determinants of this performance. Descriptive analyses followed by various statistical tests were used to analyze the characteristics of the farms surveyed. In more detail, the quantile approach was used to compare groups of farms defined according to their economic performance. An inter-group analysis was therefore carried out to highlight the factors that differentiated the most economically efficient group of farms from the other groups. In addition, an intra-group analysis using quantile regressions identified the factors that significantly influence the economic performance of farms within each group. To achieve these objectives, two survey phases were carried out, the first in 2018 and the second in 2019, on a random sample of 211 farms. The analysis of the economic performance of small-scale farming was based on data collected during the B-2019 crop year.
The results of this study show that small-scale farming in mountainous South Kivu did not perform economically during the period analyzed. It should be noted that although the activities carried out by farmers at farm level enabled them to create appreciable wealth, estimated on average at US$1,117.9, to achieve an average gross margin estimated at US$889.9 and a margin rate of 89% on average, as well as overall labor productivity estimated on average at US$3.2 per day, the net farm income (US$627.9 on average) generated by the activities remains low, for the B-2019 campaign. Sensitivity analysis showed that the farmer’s gross margin is more sensitive to a fall in selling prices than to an increase in variable production costs, with respective sensitivities of 14% and 4% when these two factors vary by 10% each.
The comparative analysis of the farm income of the farmers surveyed and the guaranteed minimum wage showed that the average farm income was slightly lower than the guaranteed minimum wage (US$4.4) in force in the Democratic Republic of Congo. If this income enabled the ordinary salaried worker to cover his basic needs, the farmer in mountainous South Kivu, whose average daily farm income is estimated at US$4.0, would not be living in precarious conditions either. However, only 34% of the farmers surveyed would be in this situation, and the farmers in the top group, i.e., 25% of the sample, are the only ones with a farm income of over US$5.6 per day. These results show that the living conditions of most of the farmers surveyed remain precarious, since they have a low daily agricultural income of less than US$4.0. However, these results are only valid in the short term, given the survey period.
Empirical analyses based on agricultural production systems have indicated that diversification of different crop types on farms positively influences their economic performance. However, to achieve a higher level of performance, the farmer will need to make a better choice between the different types of crops to be grown. Analysis of farm characteristics in mountainous South Kivu shows that farm differentiation is based on farm size, number of fields per farm, level of crop diversification, choice of production systems, cropping system, land tenure, type of farm management, type of labor used, farm income, value added, and gross margin achieved.
Intergroup analysis showed that farms in the top group, i.e. those with the best economic performance, differed from farms in the middle and bottom groups in terms of diversification of crop types, greater mobilization of family and hired labor, greater number of fields per farm, as well as the area owned and farmed, farmers’ membership of farmers’ organizations, the high proportion of female spouses involved in activities, the mobilization of mutual aid, the gender of the farmer, the high level of education of farmers and the practice of livestock rearing by the farmer. This result is corroborated by some of the characteristics of the most economically successful farm in the sample.
Analyses of the determinants of the economic performance of small-scale farming in mountainous South Kivu reveal that the factors that significantly influence the economic performance of farms in this area include the farmer’s level of education, the use of family farm labor, the area owned by the farmer, the production system, and the location of the farm. The results of quantile regressions indicated that factors such as farmer’s level of education, production system, farm location and membership of a farmers’ organization exerted a significant influence on the economic performance of farms in the middle group. On the other hand, no factor exerted a significant influence on the economic performance of farms in the top and bottom groups.