1. Introduction
Yam is a crop of great importance to at least 60 million rural smallholder farmers, processors, and consumers in West Africa [1]. Cultivation of yams by rural households contributes to household and local food supply; income generation both on farm and off-farm through value addition and marketing; and production of planting materials for personal cultivation as well as to generate additional income from the sale of surplus seed yams. With an estimated average national annual per capita consumption in 2015 of 125 kg, yam is an important and much sought-after food [2]. Domestic consumption has increased over the years from 4,288,000 MT in 2006 to 7,296,000 MT in 2015 [2]. Ghana is the second largest producer of yam in West Africa with an estimated production volume of 7.4 million tons and is also Africa’s leading exporter of yam. In 2016, exports to the United States of America (USA) were valued at 27.5 million USD. In the American market, it is the third largest exporter behind Jamaica (33%) and Costa Rica (20%). Ghana reached a 15% market share in the USA in 2016. In the United Kingdom, Ghana is the largest exporter with an over 80% market share [3].
Notwithstanding the importance of this crop, its production has been stagnating and thus threatening domestic food security and rural livelihoods due to many challenges including declining soil fertility, pests and disease, and a lack of quality planting material. The stagnant productivity also threatens processing and marketing activities along the yam value-chain and could potentially pose a risk to Ghana’s prominent economic status in the international yam trade. These constraints coupled with recurrent extreme weather events such as low and sporadic rainfall patterns directly and indirectly impact yam production, thereby threatening food security and economic growth. The struggle for fertile arable lands in particular amidst rising human population and urbanization has exerted significant pressure on existing croplands and forestlands in yam-growing areas [4]. Some farmers travel long distances in search of fertile lands and yet find it difficult to secure cultivable land. Consequently, farmers are compelled to cultivate yam on un-fallowed land leading to low yields [5]. As it is no longer feasible to expand the area under cultivation, developing and disseminating yield-increasing technologies remain the possible options to increasing yam production to sustain farm families dependent on yam cultivation and meet the growing demand. Soil-enhancing technologies like fertilizers and manures have been recommended as the way forward [5]. While these recommendations are often designed to promote sustainable soil fertility management, in many cases, the use of fertilizer alone has not promoted good soil health. In addition, it is becoming increasingly difficult for poor-resource farmers to afford and meet the fertilizer requirements on their farms due to the high cost of fertilizers and limited access to capital. Therefore, greater attention is given to alternative and sustainable approaches to maintaining soil fertility and quality for yam and other crop production.
The use of leguminous crops and shrubs has been observed as a major constituent in sustainable cropping systems, due to their biological nitrogen fixation, and therefore can be adopted in yam production [6,7]. This study proposes integrated soil nutrient management with a leguminous shrub—pigeonpea ((Cajanus cajan (L.) Millsp.)) for sustainable yam production. Pigeonpea is a leguminous crop found in Africa. While pigeonpea is not widely cultivated in Ghana, the existence of both annual and perennial varieties of pigeonpea as well as its widely environmental adoptable attributes couples with its multiple uses and benefits would complement yam production. For instance, pigeonpea plants have minimal competition in mixed cropping systems. The crop has a slower initial growth which can limit competition with the yam, the shading effect from pigeonpea can lower temperature and limits soil drying, creating a favorable growing environment for yam [8,9]. Additionally, the heavy biomass from pigeonpea represents a concentration of nutrients to improve the soil’s nutrient status [10]. The woody tree trunks would also serve as live stakes for the yam vines to climb on. Therefore, by integrating pigeonpea into yam cropping system, farmers have the potential to mitigate some of the existing production constraints. Notwithstanding, the pigeonpea-yam cropping system could affect productivity, as there could be competition for soil nutrients, moisture, and sunlight [11,12]. There could also be a potential increase in labor cost due to the training of the pigeonpea to allow climbing of vines. Pigeonpea is widely consumed in parts of Africa, Asia, and Latin America [13]. It is high in protein and contains carbohydrates, minerals, vitamins, and fiber and is identified as a nutrient-rich food source with a number of health benefits [14]. Foliage and plant residues are often used for animal feed. Thus, in addition to agronomic benefits, pigeonpea can provide benefits with regard to human and animal food and nutrition. Considering its potential, pigeonpea–yam intercropping system offers a viable option for smallholder farmers to increase and sustain their production with the potential for other benefits. This socio-economic study is a part of a larger research project aimed at assisting yam farmers to integrate pigeonpea into yam production in Ghana to improve soil fertility, income, and livelihoods. The specific objectives were to evaluate yam farmers, farm characteristics production constraints, and determine farmers’ knowledge and perception of the pigeonpea–yam cropping system.
2. Methodology
2.1. Study Area
The data for the study were collected from Ejura–Sekyeredumase municipal (ESM) in Ashanti region, Atebubu–Amantin district (AAD) and Techiman municipal (TM) areas of the Brong–Ahafo region. Figure 1 shows a map of Ghana highlighting the study areas. The Brong–Ahafo region is positioned between longitudes 0°15′ W and 2°25′ W, and latitudes 5°50′ N and 7°46′ N. It is in the forest–savanna transition agroecological zone of Ghana with bimodal rainfall ranging between 1100 mm and 1300 mm per annum. The temperature is fairly uniform ranging between 27 °C and 31 °C. The Ashanti Region is within the forest agroecological zone, with bimodal rainfall and a temperature range of 1150–1500 mm and 21–32 °C, respectively. The Ashanti Region has two major soil types, the forest and savannah ochrosols [15].
2.2. Sampling, Data Collection and Data Analysis
A multistage sampling technique was used to select the respondents. A purposive sampling strategy was used to select the districts with predominant cultivation of yam. In addition, we used a purposive sampling of five villages from each district based on levels of yam production. Finally, a simple random sampling was used to select 10 yam farmers from each of the selected villages making a total of 150 farmers. The sample frame consisted of all yam farmers. Using a structured questionnaire, information on general household and socio-economic characteristics (e.g., age, education, gender, farm ownership, land size), nature of farming and input access, and institutional factors (e.g., market access, access to extension) were collected. In addition, information on farmers’ knowledge and use of pigeonpea were collected. Trained enumerators were deployed to administer the questionnaire after being informed of the objectives of the study and contents of the survey. Subsequently, data analyses were carried out in STATA 14 software (14.2, Stata Corp LLC, College Station, Texas 77845, USA). Descriptive statistics (descriptive, frequencies, cross tabulations), differences in means, charts and inferential statistics involving regressions were used to conduct a multifaceted analysis.
2.3. The Empirical Model
The logit regression model was used to determine the factors that influence farmers’ knowledge of pigeonpea. This is due to the dichotomous nature of the dependent variable (knowledge), which is either the respondent has knowledge on pigeonpea or otherwise. Being based on the cumulative logistic probability function t, the logit model can be used for transforming the dependent variable to predict probabilities within the bound (0, 1) [16]. The assumption here is that each farmer faces a set of discrete, mutually exclusive outcomes of knowledge, which are conditioned by a set of explanatory variables . The estimated model would enable a researcher to assess how changes in explanatory variables affect the response variable knowledge denoted as . We set up the following discrete choice model:
(1)
where is the response variable, is the error term, the conditional probability takes the logistic form:(2)
where is the coefficient, is the identified factor contributing to the knowledge acquisition on pigeonpea.3. Results and Discussion
3.1. Farmer Demographic Structure
Table 1 presents the demographic characteristics of yam farmers from the three yam-producing districts selected for this study. Of all the farmers contacted, 62% were males and 38% were females. From the Atebubu–Amantin district, 74% of the respondents were males and only 26% were females. Similarly, from the Techiman municipal area, 64% of the respondents were males and 36% were females. The result is not surprising as women’s ownership to farmlands in most parts of Ghana is limited due to the system of inheritance where land is almost always bequeathed to males. The consequence is that women’s access to and use of land is through their male counterparts [17]. On the contrary, in the Ejura–Sekyeredumase municipal, 52% of the respondents were females and 48% were males. The result here may be due to the fact that in that district and region the matrilineal system of inheritance persists and women’s ownership to land is unrestrained. The majority (89%) of the farmers were married with an average family size of 8 persons. This is higher than the national average family size of 4.4 persons [18]. The Atebubu–Amantin district had farmers with an average family size of 9 persons, followed by the Techiman municipal area and then the Ejura–Sekyeredumase municipal with an average family size of about 8 persons. On the level of education, 44% of farmers in Techiman municipal had junior high school level education, followed by the Ejura–Sekyeredumase municipal and the Atebubu–Amantin district with 36% and 18%, respectively. Only 7% of farmers had up to senior high school level of education across the districts. The Atebubu–Amantin district had 54% of farmers with no formal education, while the Ejura–Sekyeredumase and Techiman municipal areas had 36% and 28% respectively of farmers with no formal education.
The average age of a yam farmer was about 47 years. The Ejura–Sekyeredumase municipal had the oldest farmers with an average of about 50 years followed by the Atebubu–Amantin and Techiman municipal with 45 years. Compared to the national average age of a farmer, which is 55 years [19] the farmers who participated in this study were younger. Regarding experience in yam farming, farmers had an average experience of 18 years. Farmers from the Atebubu–Amantin district were the most experienced in farming with about 22 years followed by farmers from the Ejura–Sekyeredumase municipal with 18 years and then farmers from Techiman district with 15 years of experience.
3.2. Farm Characteristics
Farm characteristics by district are shown in Table 2. The average farm size was 3.28 acres (1.3 hectare (ha)). The Atebubu–Amantin district had the farmers with the largest average farm size of 5.57 acres (2.23 ha). Average farm sizes in the Techiman municipal and the Ejura–Sekyeredumase municipal were 2.45 acres (1 ha) and 1.8 acres (0.72 ha), respectively. The Ministry of Food and Agriculture of Ghana reports that the average farm size in Ghana is about 2 hectares (about 5 acres) [2]. The results from this study indicated that farmers across the study area had less than average land holdings (Table 2). It was not surprising that in the Ejura–Sekyeredumase district where the majority of the farmers interviewed were women, the average farm size was even smaller (Table 2). Due to the drudgery in yam farming and limited resources, women are unable to cultivate large farms. Awareness of such gender differences is essential in formulating interventions so that technologies and training programs are developed to address the challenges faced by those with resource limitations.
The distance from home to farm averaged at 3.7 km, which was quite substantial. A farmer from the Atebubu–Amantin district had to trek an average distance of 6.8 km to the farm, followed by farmers from the Ejura–Sekyeredumase and Techiman municipal areas who had to trek an average distance of 2 km from home to farm. Compared with the Ejura–Sekyeredumase and Techiman municipal areas, the Atebubu–Amantin district had a larger land area and was more sparsely populated [18]. Thus, farmers are able to go further in search of fertile lands. Farmers mostly had to walk these distances; a majority (46%) said they walked to their farms (Table 3). This finding is in line with a number of studies showing that farmers travel long distances in search of fertile lands for yam production [5,20].
The average yam yield per acre was 4019 kg/acre (10 t/ha). This was less than the national average yam yield of 16 t/ha [2]. Comparison of yields across the districts showed that farmers in the Techiman municipal reported the highest at 4481 kg/acre (11.2 t/ha), followed by those in Atebubu–Amantin district with 4222.33 kg/acre (10.55 t/ha). The Ejura–Sekyeredumase municipal had the lowest yam yield of 3354.85 kg/acre (8.38 t/ha). As indicated in Figure 2, farmers claimed that they lacked access to fertile lands. In fact, a majority (58% and 50% from Atebubu–Amantin and Techiman respectively) of farmers reported that their lands were very fertile. Interestingly, the land fertility status was primarily based on yields and not on other soil quality-related attributes (Figure 3). Land productivity is influenced by land/soil attributes and the most important factor is management. This result raises some important questions about farmers’ understanding and knowledge of land quality, land management, and productivity. Farmers training in management practices and soil quality estimation would be important.
Nonetheless, farmers adopted some land management practices that helped regenerate the soil fertility and soil organic matter (Figure 4). For instance, shifting cultivation was practiced widely by farmers in the study areas where plots of land are cultivated temporarily, then abandoned and left to remain fallow allowing them to revert to their natural vegetation. The challenge was that farmers were not able to wait long enough for the land to fully regenerate due to limited availability and an urgent need for land for farming. Farmers in all three districts practiced shifting cultivation with the majority in the Atebubu–Amantin district (74%), followed by Ejura–Sekyeredumase (70%) and Techiman municipal area (64%). The next most popular soil management practice was rotation with a leguminous crop. Eighteen percent (18%) of farmers from both the Ejura–Sekyeredumase and Techiman municipalities rotated their yams with leguminous crops. Very few farmers from the study area used chemical/inorganic fertilizers on their land (Figure 4). This was mainly due to a lack of capital and the perception among farmers that chemical/inorganic fertilizers changed the taste and shelf life of yam [7].
3.3. Constraint on Yam Production
Data were collected on the constraints to yam production in the study area. The questions were designed to fit a Likert scale, with farmers scoring statements on a measurement scale of 1 to 3 with 1 = not severe, 2 = severe, and 3 = very severe. The responses were then ranked by assigning weights from one to three in declining order, such that rank one had the highest weight of three and rank three had the lowest weight of one. Following [21], the overall score for each constraint was then calculated by summing up the number of farmers that mentioned the constraint multiplied by the rank position assigned to the constraint. Table 3 shows the ranking of constraints by the yam farmers. The ranking of the constraints in ascending order revealed that access to capital, access to mechanization, and poor road network were the top three ranked constraints of yam farmers.
According to the farmers who participated in this survey, access to capital was their primary constraint which hindered them from purchasing necessary inputs to fully meet production needs and make investments to enhance farming activities. Access to capital is further inhibited by high interest rates ranging from 19–25% on borrowed capital [22] and the lack of collateral especially among female farmers. The risky nature of rain-fed agriculture further aggravates the situation, as return on investments can be volatile and highly susceptible to biotic and abiotic stresses. On mechanization, farmers were unable to access machinery and tractor services due to a limited number of tractors across the districts. It is estimated that Ghana had about 2000 tractors in 2004 [23] and the numbers are dwindling due to poor maintenance and because farmers may not be able to purchase new tractors due to financial limitations. Moreover, fragmented and small parcel sizes of less than 2 ha per farmer make mechanization difficult and costly [2]. On poor roads, generally, roads in rural areas are not in good condition and sometimes farms are not easily accessible. This significantly impacts farmers’ ability to sell their yam in a timely manner as buyers and their vehicles are unable to reach distant and disconnected farms to purchase fresh produce.
Mounding and access to stakes were the fourth and fifth constraints to yam production respectively. Manpower was used to prepare mounds in the study area. Farmers needed to pay for hired laborers to get mounds prepared. This adds to production costs and it was found that, in the study regions, an average of GH₵ 1.40 was used to prepare a seed bed during the 2016 production year. Access to stakes was a challenge to farmers due to the long distance farmers had to travel in search of stakes in the study area. Any intervention to help make available stakes to farmers will very much enhance the efficiency of yam production in the study area. Prolonged drought and access to land were sixth and seventh constraints mentioned by farmers. Ghana’s agriculture is rain-fed, and climate variability has brought about unusual rainfall patterns that affect production. Access to cultivable land is also a challenge mainly due to population pressures. Farmers are forced to cultivate on the same piece of land for extended periods resulting in soil degradation, which consequently reduces yam yields.
Technical know-how was ranked as the eighth constraint affecting yam production. Farmers are good with what they know but lack access to recent innovations and methods, such as integrated soil fertility and weed management, reduced staking options for yam production, use of mechanized ridging instead of mounds in yam production, etc., that would help them to manage and increase their cropping activities. The majority of farmers are not given an opportunity to participate in demonstrations and field days due to resource constraints. The lack of farmer participation in such learning activities hampers the adoption of new agricultural technologies. As farmers have limited capital for the initial investments needed to adopt new technology and also lack motivation as they have little knowledge on how the technology fits into their cropping systems, they tend to reject it. However, through consultative participatory research approach, farmers will have the option to co-learn and co-design with researchers and consequently help overcome such difficulties as farmers become active participants in the research project and are able to see and evaluate the performance and outcomes of the new technology over their existing ones.
Pests and diseases were ranked as the ninth constraint affecting yam production. Notable yam diseases are viruses, anthracnose, and nematodes on the tubers. There is no organized seed system for root and tuber crops in Ghana. Farmers use their own saved seeds or purchase them from the market. The seed yams are local varieties, which have been used over time and have become susceptible to diseases and pests [24]. This coupled with declining soil fertility affect yield drastically. Access to fertilizer and seed yam constraints was ranked tenth by yam farmers. Fertilizers are available but not affordable. Farmers avoid the use of fertilizers in yam production due to financial difficulty and perception of taste change with the use of fertilizer. On seed yams, farmers use both ware yam and seed yam for production. However, they mostly use local varieties that do not guarantee quality and good yield. Access to extension services was the final constraint mentioned by farmers to affect the production of yams. Extension personnel may be available in farmers’ communities but may not be providing the services that farmers need. As seen from the results, farmers were concerned about technical know-how and the available extension agents might not be providing those needs to them. The extension agents might be lacking training on the technical know-how due to lack of funding.
3.4. Knowledge of Pigeonpea–Yam Cropping System
The survey contained questions to seek farmers’ knowledge of pigeonpea and uses of pigeonpea. Table 4 shows a summary of this information by district. A majority (54%) of farmers across the districts had no knowledge of pigeonpea and less than half of the farmers had knowledge of the use of pigeonpea. The Atebubu–Amantin district had the majority (68%) of farmers who had no knowledge of the crop followed by Techiman municipal area with 50% and Ejura–Sekyeredumase district with 44%. On the knowledge of uses of the crop, 75% of farmers across the districts confirmed having no knowledge of the use of pigeonpea while about 25% had knowledge of the use of the crop. In the Atebubu–Amantin district 80% of farmers had no knowledge of the use of the crop, while in the Ejura–Sekyedumase district and Techiman municipal area it was 78% and 68%, respectively. This lack of knowledge of both the crop and its uses must be taken into account when developing interventions involving pigeonpea including programs to disseminate information about the intercropping system and farmer awareness.
Table 5 shows the knowledge of usages of pigeonpea. Interestingly, across the districts, a majority (62%) knew that the crop could be used for food. About 20% knew it could be used as a border crop and 9% knew it could be used as an intercrop with other crops. There is a gross lack of knowledge among farmers considering the multiple benefits from pigeonpea such as the provision of biomass, production of a nutritious grain rich in protein, and high nitrogen-fixing capacity [25]. The survey revealed that pigeonpea was not cultivated in the study area as none of the respondent farmers had ever cultivated or consumed it before. Their awareness of the crop mainly resulted from their travels to other regions. Previous research on pigeonpea in Ghana shows the crop is not completely new [26,27,28]. However, these studies did not push through with farmer adoption and this has led to a limited knowledge of pigeonpea by farmers. With field demonstrations, farmers can see for themselves the uses of pigeonpea as a live stake, a soil nutrient enhancer, as green manure, a food crop, and a source of feed for livestock. This will help encourage them to adopt pigeonpea and reap the benefits to boost their farm production. This would have a ripple effect of improving smallholder farmers’ income, livelihoods, food, and nutrition security.
3.5. Factors Influencing Farmers’ Knowledge of Pigeonpea
Table 6 shows logit estimates of factors influencing knowledge of pigeonpea by district. The results revealed that the probability of knowing pigeonpea was affected by household size in the Atebubu–Amantin district. The effect was negative and significant, implying that farmers with a small family size are more likely to know pigeonpea. These farmers might be less engaged and might be more interested in new introductions. In Ejura–Sekyedumase, knowledge on pigeonpea was associated with age, household size, number of dependents, and years of education. The implication here is that farmers with large family size are more likely to know pigeonpea. These may be farmers who want intensification to satisfy family needs. Younger farmers and farmers with lower levels of education were also associated with knowledge on pigeonpea. These may be farmers with low capital and limited land who may want intensification to increase yield and income. Awareness creation to increase knowledge and subsequent adoption of pigeonpea is important for all categories of farmers, as was found in Tanzania [29].
4. Conclusions and Implications
The study has investigated yam farmers’ characteristics, their constraints against yam production, and their knowledge of pigeonpea. The results revealed that farmers had an average farm size of 3.28 acres (1.3 ha). The Atebubu–Amantin district had the highest mean farm size of 5.57 acres (2.23 ha). The average farm size of farmers from the Techiman municipal and Ejura–Sekyeredumase districts was 2.45 acres (1 ha) and 1.8 acres (0.72 ha), respectively. The farm size was lower than the national average of 2 hectares. Small farm size coupled with the limited use of improved technologies such as improved varieties and management practices has resulted in low yields. Average yield per acre was 4019 kg (10 t/ha). Yields from the Techiman municipal area were highest at 4481 kg/acre (11.2 t/ha) and that from the Atebubu–Amantin district was 4222 kg/acre (10.55 t/ha). The yields were lower than the national average of 16 t/ha. The need for the introduction of new technologies to improve yields is very important.
The results showed that farmers generally practiced shifting cultivation but are unable to allow the land enough time to fallow. This had led to low yields. The results also revealed some farmers intercropped yam with leguminous crops such as cowpea and soybean which could be used as food and feed. However, they found getting access to stakes difficult, which are very important for yam production due to afforestation. Pigeonpea is a leguminous crop that has the ability to fix soil nitrogen and provide stakes and could help farmers. Results from the study showed that a majority (54%) of farmers across the district did not know pigeonpea and those who knew the crop did not use the crop in their cropping system. Since pigeonpea has multiple benefits such as the provision of food, use as stake, fodder for livestock and can also fix soil nitrogen, farmers should be encouraged to integrate it into their cropping systems.
Farmers were also faced with many constraints that affected yam production. These constraints included access to capital, poor road network, access to mechanization, labor intensive nature of mounding, technical know-how, and access to stakes, access to seed yam, and pest and diseases. We envisage that policy intervention in alleviating these constraints will boost yam production for household consumption and for exports.
Farmers’ characteristics such as age, household size, number of dependents, and years of education were found to be associated with knowledge of pigeonpea. Since pigeonpea has multiple uses it holds high potential for enhancing food security, agricultural productivity, and livelihood. Awareness creation to increase knowledge and subsequent adoption of pigeonpea is important for all categories of farmers. Moving forward, we envision adopting a consultative participatory approach to work closely with yam farming communities to design appropriate sustainable systems to address current constraints of yam production.
Author Contributions
P.P.A. collected data, performed the analysis, and drafted the paper; E.O.D. reviewed literature and reviewed paper; P.H. participated in data collection, reviewed literature, and the paper; H.G.D. and C.W. designed the research and revised the paper; all authors contributed to the questionnaire design and have read and approved the final paper.
Funding
This work was funded by the Alliance for African Partnership (AAP) of Michigan State University.
Acknowledgments
We thank Enoch Bessah for illustrating the map of the study area. We Acknowledge Borlaug Higher Education for Agricultural Research and Development (BHEARD) for supporting the study. We also acknowledge the contributions from the technicians from the socio-economics section of CSIR—Crops Research Institute during the data collection.
Conflicts of Interest
The authors declare no conflict of interest.
Figures and Tables
Enlarge this image.Figure 3. Distribution of farmer based on their assessment of soil fertility indicators by district.
Demographics characteristics of farmers in the three districts.
| Characteristics | AAD | ESM | TM | All |
|---|---|---|---|---|
| (N = 50) | (N = 50) | (N = 50) | (N = 150) | |
| Sex of farmer (%) | ||||
| Male | 74.00 | 48.00 | 64.00 | 62.00 |
| Female | 26.00 | 52.00 | 36.00 | 38.00 |
| Marital status (%) | ||||
| Single | 6.00 | 0.00 | 2.00 | 2.70 |
| Married | 94.00 | 84.00 | 90.00 | 89.30 |
| Divorced | 0.00 | 12.00 | 6.00 | 6.00 |
| Widowed | 0.00 | 4.00 | 2.00 | 2.00 |
| Educational level (%) | ||||
| Up to primary | 18.00 | 22.00 | 30.00 | 20.00 |
| Up to junior high | 18.00 | 36.00 | 44.00 | 32.70 |
| Up to senior high | 10.00 | 6.00 | 6.00 | 7.30 |
| Post senior high/tertiary | 0.00 | 0.00 | 2.00 | 0.70 |
| No formal education | 54.00 | 36.00 | 28.00 | 39.3 |
| Other farmer characteristics (mean) | ||||
| Household size (count) | 9.26 | 7.58 | 7.94 | 8.26 |
| Number of dependents (count) | 2.53 | 3.28 | 3.02 | 2.96 |
| Age of farmer (years) | 45.42 | 49.82 | 44.64 | 46.62 |
| Years of formal education | 3.92 | 4.96 | 5.86 | 4.92 |
| Experience in yam farming (years) | 21.68 | 18.36 | 15.02 | 18.37 |
| Years of residence | 24.24 | 30.08 | 23.52 | 25.94 |
Farm characteristics in the three districts.
| Farm Characteristics | AAD |
ESM |
TMBA |
All |
|---|---|---|---|---|
| Average Farm size (acres) | 5.57 | 1.80 | 2.45 | 3.28 |
| Average yield (kg) per acre | 4222.33 | 3354.85 | 4481.45 | 4019.55 |
| Average Distance from home to farm (km) | 6.85 | 2.26 | 2.08 | 3.70 |
| Means of transport (% of farmers) | ||||
| Walking | 32.00 | 52.00 | 54.00 | 46.00 |
| Bicycle | 54.00 | 14.00 | 28.00 | 32.00 |
| Motorbike | 14.00 | 32.00 | 18.00 | 21.33 |
| Car | 0.00 | 2.00 | 0.00 | 0.67 |
Ranking of constraints to yam production.
| Constraint | Very Severe | Severe | Not Severe | Total Score | Overall Rank | |||
|---|---|---|---|---|---|---|---|---|
| Freq. | Score | Freq. | Score | Freq. | Score | |||
| Access to capital | 75 | 225 | 73 | 146 | 2 | 2 | 375 | 1 |
| Access to mechanization | 85 | 255 | 27 | 54 | 38 | 38 | 347 | 2 |
| Poor road network | 77 | 231 | 17 | 34 | 56 | 56 | 321 | 3 |
| Labor for mounding | 35 | 105 | 78 | 156 | 37 | 37 | 298 | 4 |
| Access to stakes | 35 | 105 | 73 | 146 | 42 | 42 | 293 | 5 |
| Prolong drought | 28 | 84 | 77 | 154 | 45 | 45 | 283 | 6 |
| Access to land | 42 | 126 | 45 | 90 | 63 | 63 | 279 | 7 |
| Technical know how | 20 | 60 | 68 | 136 | 62 | 62 | 258 | 8 |
| Pest and diseases | 15 | 45 | 77 | 154 | 57 | 57 | 256 | 9 |
| Access to fertilizer | 20 | 60 | 44 | 88 | 86 | 86 | 234 | 10 |
| Access to seed yam | 28 | 84 | 28 | 56 | 94 | 94 | 234 | 10 |
| Labor for ridging | 22 | 66 | 32 | 64 | 96 | 96 | 226 | 11 |
| Access to extension | 6 | 18 | 54 | 108 | 90 | 90 | 216 | 12 |
Field survey, 2017.
Table 4Knowledge and use of pigeon pea by district.
| AAD |
ESM |
TMBA |
All |
|
|---|---|---|---|---|
| Knowledge of pigeon pea (%) | ||||
| Yes | 32 | 56 | 50 | 46 |
| No | 68 | 44 | 50 | 54 |
| Knowledge of pigeon pea use (%) | ||||
| Yes | 20 | 22 | 32 | 25 |
| No | 80 | 78 | 68 | 75 |
Field survey, 2017.
Table 5Knowledge of the uses of pigeonpea by district.
| For Intercropping | As a Natural Border or Hedge Crop | As a Commercial Crop | For Fodder | For Food | |
|---|---|---|---|---|---|
| AAD (N = 14) | 0.00 | 14.29 | 0.00 | 14.29 | 71.43 |
| ESM (N = 28) | 21.43 | 25.00 | 7.14 | 3.57 | 42.86 |
| TMBA (N = 24) | 0.00 | 16.67 | 0.00 | 4.17 | 79.17 |
| Across Districts | 9.01 | 19.70 | 3.03 | 6.06 | 62.22 |
Field survey, 2017.
Table 6Logit estimates of factors influencing knowledge of pigeonpea.
| AAD |
ESM |
TMBA |
||||
|---|---|---|---|---|---|---|
| Coeff. | Std. Error | Coeff. | Std Error | Coeff. | Std Error | |
| Gender | 0.258 | 0.854 | 0.216 | 0.852 | −1.044 | 0.780 |
| Age | 0.060 | 0.049 | −0.071 * | 0.043 | 0.000 | 0.042 |
| Marital status | 0.661 | 1.806 | −0.593 | 0.896 | 0.000 | 0.000 |
| Household size | −0.215 * | 0.134 | 0.288 * | 0.1614 | 0.123 | 0.119 |
| Number of dependent | 0.049 | 0.190 | 0.373 * | 0.216 | 0.100 | 0.187 |
| Years of education | 0.089 | 0.082 | −0.217 ** | 0.102 | 0.121 | 0.079 |
| Experience | −0.038 | 0.043 | 0.017 | 0.048 | −0.069 | 0.050 |
| Years of education | −0.037 | 0.025 | 0.031 | 0.029 | 0.005 | 0.031 |
| Constant | −1.962 | 3.600 | 1.435 | 2.623 | −0.645 | 1.776 |
| Pseudo R2 | 0.158 | 0.280 | 0.122 | |||
** significant at 5%; * significant at 10%.
© 2019 by the authors.