The large variation in the selling price of guinea fowl observed depended on the region and period of the year. In southern Benin, guinea fowl was more expensive due to the proximity to the urban centers and the low availability of the birds in this region. As for Northern Benin, the reluctance of farmers to sell guinea fowl in the rainy season because of it coincided with the reproduction period of these birds coupled with the higher demand of the birds during the festive period including Chrismas, New Year celebration and Easter, are the causes of the hike in the selling price of guinea fowl. The price increase ranged from 180 FCFA to 725 FCFA on the average. However, white guinea fowl were generally sold at higher prices than other phenotypes because they were in greater demand during religious ceremonies. Houndonougbo et al. also found that white guinea fowl had a higher selling price than those of other phenotypes of guinea fowl. Guinea fowl eggs were generally sold between 65 FCFA and 150 FCFA . The egg-laying period in guinea fowl was seasonal and lasted from April to October. During this period, mature guinea fowl were likely to lay an average of 71 16 eggs distributed over 26–30 weeks at about 6–7 months of age. Contrary to this observation, guinea fowls have been reported to lay eggs during the dry season in Botswana . Egg incubation were mostly natural and lasted from 26 to 29 days while the duration varied from 26 to 28 days according to some authors.This incubation period also varied from 27 to 28 days in Ivory Coast and Bangladesh.This difference in duration can be attributed to the climatic conditions which differ from one country to another thus affecting embryonic development.
The average hatchability rate was 74%. This hatching rate was relatively similar to earlier report in Benin , dutch buckets but higher than what was observed in Zimbabwe as documented by Zvakare et al. . The average weight of guinea fowl obtained was lower than that obtained by Ogah in Nigeria, but higher than that reported in Ghana . This average weight varies by region. These variations in weight may be due to the environmental conditions which differ from one region to another and which can be favorable or not to the good growth of guinea fowls. In rural areas, a mortality rate of 10% was observed one week after hatching. This mortality rate could be as high as 22% at 3 months of age. To limit these mortalities, farmers used the bark and leaves of certain locally available plants which are macerated and included in the drinking water of guinea fowl. Some of these plants materials employed by farmers during the survey include Azadirachta indica and Khaya senegalensis. Old practices relating to the use of traditional medicine are still relevant because of the low income of farmers and their distance from urban centres. Nevertheless, the use of traditional medication still has its drawback in most cases due to non-precise diagnosis and medication dosage . Therefore, it would be necessary to verify the effectiveness of these ethno-veterinary plants in order to validate for a better recommendation . About the characterization of guinea fowl farming system, the results of the present study made it possible to identify four categories of guinea fowl farmers in Benin which differed based on location, sex, level of education, activity carried out and type of incubation. In Alibori region, where guinea fowl production in general constituted the primary occupation of respondents, followed by crop production, women were moderately involved in guinea fowl rearing. This result is explained by the involvement of more men in large ruminants production, which they believe was more profitable. In this region, the incubation of eggs was almost natural through the involvement of mother hens, ducks and turkeys.
On the other hand, women were fairly involved in guinea fowl farming in the region of Atacora where agriculture was the dominant activity but associated with guinea fowl production. Guinea fowl farming was mainly engaged in by men in Benin unlike the case of Zimbabwe where women were more involved in this sector. Individuals with a high level of education were involved in guinea fowl rearing as a secondary activity. These results suggest that guinea fowl production was mostly done by illiterates, who had more empirical experience in the field as also reported by Kwesisi et al. . In terms of comparison of the four groups of guinea fowl farmers, it can be deduced that cluster 3 showed the best performance. It is made up of mostly young farmers between 25 and 50 years of age with middle experience in guinea fowl breeding. Although most of the respondents in this group are represented in almost all regions, they are best found in the Borgou, Couffo and Mono regions. These guinea fowl farmers mostly adopt a semi-intensive breeding system and use artificial incubation to hatch their eggs better than others clusters. Cluster 4 farmers, who were better represented in Atlantique, Collines and Zou regions, took the second place based on these variables . The farmers in cluster 2 took the third position and those in cluster 1 who were relatively women occupy the last place in this classification. Any capacity building and support program should be aimed at these clusters of farmers in order to get more women involved in guinea fowl farming and ultimately increase the productivity of the species. However, in Botswana, Moreki et al. reported that women were mainly beneficiaries of guinea fowl projects. This situation, although deliberately targeted women, demonstrated that women can also raise guinea fowl. This last cluster had more illiterate farmers than all other groups. This is part of the reason for their poor performance. Nevertheless, this high rate of illiteracy, which is not peculiar to guinea fowl production, is a potential disadvantage for large-scale production of guinea fowl because of its negative influence on the adoption of new technologies .
Land reform programmes are initiatives in which nations attempt to correct inequalities in ownership and access to land, by re-allocating the land from the land-endowed to the landless , 2000; World Bank, 1975. Previous landowners may be compensated for their land, during the implementation of these initiatives. Globally, countries which implemented agrarian reform or land reform have struggled to attain synergy between the social and economic objectives of land reform programmes . In the past two decades, land reform implementation in South Africa experienced such a dilemma ; different sub-programmes were implemented with different objectives and a diversity of outcomes can be observed. In South Africa , the initial programmes were socially oriented, and this resulted in social diversity of new landowners. However, in recent years, the programmes aimed at establishing farmers with good economic performance by giving land mainly to those with financial resources to use it . Not only institutional drivers can be attributed to the existence of land reform farms since the farms also vary for example, in natural capital and physical capital endowments. Variations in institutional drivers, and natural and physical capital endowments of land reform farms are anticipated to influence agricultural land use and the success of policies and interventions implemented for further development of these farms . Farming system research is applied to better understand agricultural land use, its drivers and to design strategies for development . Further, farming system research focuses on decisions regarding production and consumption taken by a farming household . In this study, we consider a farming system to be “a population of individual farm systems that have broadly similar resource bases, enterprise patterns, household livelihoods and for which similar development strategies and interventions would be appropriate”.Identifying farming system types allows a shift from broader generalisation towards targeted, context-based development approaches based on identified challenges and opportunities, which may differ among types .
The types of variables used to explore farming system diversity vary and depend on the purpose of the classification . Farming system typologies are of two kinds: structural which focuses on structural variables and functional which focuses on decisions made by farmers regarding production and consumption . Statistical methods used to explore farming system diversity often include a combination of multivariate analysis with cluster analysis and Bayesian systems . These methods group farms around key characteristics with an aim to increase variation between groups and to decrease it within a group. To our knowledge, no studies have systematically classified farming system types in land reform farms of SA, grow bucket and we envisage that the results will contribute towards sustainable economic use of these farms. The aim of this study is to generate systemic knowledge on farming systems in land reform farms of the Waterberg District in South Africa . Towards this aim, we identified principal variables underlying the diversity in land use, classified farming system types, characterised the identified types, and analysed the drivers of the diversity among types. We conducted the study in the Waterberg District Municipality of the Limpopo Province, South Africa .In each of the surveyed farms, we targeted at least 15% of the ‘active households’ for data collection. A household was considered active when it had ‘at least one household member on a beneficiary list2 of a farm and also at least one household member involved in farm management or land use’. The distance between farms and the nearest urban centres were recorded and were considered proxy for ‘farm location’. Three locations were identified: the urban location at less than 16 km distance, peri-urban location between 16 and 40 km and rural location at above 40 km.
Using semi-structured questionnaires, we interviewed respondents who are either household heads or their representatives. We collected qualitative and quantitative data for the 2013/2014 agricultural year by asking recall data for the 12 months before the date of interview. Data about the agricultural activities being practiced being livestock farming , horticulture farming and crop farming , and combinations of these activities, and the land use associated with each of the agricultural activities, were collected under land use. Data about agricultural commodities produced, quantities produced, quantities sold and produce not for sale were collected under income generation. Data about the use of production factors and associated costs were collected under production costs. In the study area, production inputs were acquired mainly from formal markets, whereas agricultural produce was sold on both formal and informal markets. Remuneration of hired labour was pre-determined4 in this study, as it was governed by the Basic Conditions of Employment Amendment Act, no 20 of 2013 , 2014. This paper adopts the descriptions of formal and informal markets as given by Ferris et al. . Informal markets operate outside of the taxation system, with no prescribed quality standards and volumes of goods, whilst the opposite suffices for formal markets. Examples of informal markets for produce are sales which take place at farm gate, roadside, village and rural gathering, and examples of formal markets on the other hand, comprises retailers, fresh produce markets and livestock auction. For each of the agricultural commodities produced, data about the type of market used to sell the produce was collected under market type for produce. We conducted focus group discussions with representatives of active households to collect data about farm organisational arrangements, farm physical capital endowmentand households’ access to farms’ natural, physical, financial and social capitals. In farms owned by households individually, data about farm physical capital endowment was collected from the respondents. In instances where respondents were unsure, transect walks were taken to verify the existence of listed activities and to assess the extent of agricultural land use. To understand the drivers of farming systems, we cross-examined the findings of this study on farm organisational arrangements, farm physical capital endowment and market types for produce. The knowledge generated from those cross-examinations was used to make deductions about the influence which the aforementioned factors had on the presence and emergence of farming system types. Table 3 provides description of variables used for PCA.