1. Introduction
Agricultural productivity gains have significantly contributed to economic growth and poverty reduction in many parts of the world, but these gains have been much more limited in Sub-Saharan Africa (SSA) [
1,
2,
3]. Increased technology adoption has the potential to contribute to economic growth and poverty alleviation amongst the poor, especially in SSA, provided that successful strategies for addressing the constraints on their adoption are followed [
1]. The adoption of the innovation platform approach in several SSA countries proved to be an effective method for adopting and applying improved agricultural technologies and offered a practical model of how stakeholders, including farmers, can act and interact to address common concerns, share responsibilities and obligations, and achieve their goals [
4,
5,
6,
7].
Sudan is a predominately agricultural economy. Agriculture is Sudan’s most important economic sector in terms of its contribution to both the GDP and employment. In 2016, agriculture contributed about 31.5% of the GDP, employed about 70% of the population, and provided the livelihood for almost all rural people [
8]. Sudan has vast and diverse agricultural resources that provide various means of sustaining the people’s livelihood. Both irrigated and rainfed agriculture exist in Sudan. Sudan’s irrigated schemes are among the largest in the world. Irrigation water resources include the Blue Nile, the White Nile, other territories, and seasonal rivers, lakes, and swamps.
Wheat (
Triticum aestivum L.) is the most widely grown crop in the world and the second largest calorie source behind maize [
9]. In Africa, the gap between wheat supply and consumption is steadily increasing. For instance, during the period 2013–2022, Africa produced an average of about 27.1 million tons of wheat and imported an average of about 45.8 million tons per year, with an average value of 13.5 billion USD (
https://www.fao.org/faostat/en/#data, accessed on 1 February 2024). Urbanization and gradual income growth in SSA, including Sudan, have shifted dietary habits from the traditional cereals (sorghum, millet, and maize) and tubers (cassava, sweet potato, and yam) to wheat and rice [
10]. By 2050, wheat is expected to gain further importance as a major cereal in the diets of SSA countries, with increasing per capita wheat consumption rates exceeding those of other cereals [
11]. Wheat yield gap analyses have identified significant opportunities to increase wheat production in Africa through improved genetics and agronomic practices, which could reduce import dependence and increase national self-sufficiency in many African countries [
12].
Wheat production in Sudan started in the fertile alluvial soils of the Nile in Northern and River Nile states, where winter is relatively longer and cooler. Since the 1960s, however, wheat production has extended southward, and the crop is now cultivated in the central plains of the country at latitudes 14–15° N [
13]. Wheat is considered a strategic cereal food crop and is ranked second in terms of consumption after sorghum [
6,
13,
14]. Since the early 1960s, wheat consumption in Sudan has been sharply increasing as a result of the ever-increasing demand for wheat arising from rapid urbanization, changes in consumer habits, high consumer subsidies, and population and income growth [
15]. As a result of these factors, wheat consumption in Sudan increased from about 220 thousand tons in 1971 to over 800 thousand tons in 1991 and reached about 2.4 million tons in 2016 [
8]. This situation entailed importing 80% of the wheat to reduce the gap between local production and consumption.
Wheat production has grown slowly, primarily because of the low use of recommended inputs and poor crop management practices. Average yields are generally low compared to other producing countries, as they are affected by many production and environmental factors. One of the primary reasons for low wheat yield and the wide gap between potential and farmers’ yield is the slow adoption of the recommended improved production technologies and practices [
16]. However, with continuous research and technology transfer efforts, notable improvements have been achieved [
13]. Due to activities related to the generation of agricultural technologies, the dissemination and adoption of farming technologies, and the capacity strengthening of some projects, the yield and income of wheat-producing farmers have increased [
5].
Generally, the management practices of the farmers are relatively poor, the crop cultivation procedures are inappropriate, and inputs such as certified seeds, fertilizers, weeding, irrigation, and pesticides are minimally used in recommended doses and times. Traditional varieties are grown in some parts of the country, resulting in low current productivity (2.6 tons/ha). Fluctuating wheat prices and the typically low prices at harvest time when there is no government intervention are also serious problems for producers. Hence, wheat production is a function of all technical, financial, and socioeconomic factors. Therefore, to enhance and expand wheat production, farmers need to improve the efficiency of their poor cultural practices. In doing so, an impact assessment of production efficiency is essential for determining the areas that require improvements in the production activities, which necessitates conducting such important ex ante and ex post studies.
In addition to the factors mentioned above, climate change poses major challenges for sustainable wheat production [
17]. In Sudan, upward trends in both the annual and growing season temperatures were found when trend analyses of temperature indicators and wheat yields were used [
18]. Wheat yields in Sudan were found to be negatively associated with both day and night temperatures during the growing season, and the negative effect of rising temperature on wheat yield has increased in recent years [
18]. It is paramount to note that in stressful environments, such as those of most wheat-producing areas in Sudan, integrated crop management strategies should be strictly followed for sustainable and profitable wheat production. In the context of this changing climate, several factors are known to influence farmers’ adaptation and adoption of improved technologies. The farmer’s experience in farming, their access to extension services and climate information, access to subsidies, farm and family size, gender, and electricity for irrigation were found to be the significant factors affecting adaptation decisions in several SSA countries [
19,
20,
21,
22]. A lack of information on adaptation methods, financial constraints, limited access to climate information, and the low price of the product represented major adaptation barriers [
20,
22,
23].
In Sudan, most of the studies focused on the impact of improved technologies on wheat production and farmers’ income as well as the technological efficiency of wheat farms. However, how different technological, logistical, and socioeconomic factors affect the adoption of improved wheat technologies has not been fully explored. When factors limiting the adoption of improved technologies are identified and appropriately addressed, wheat productivity is expected to improve, ultimately leading to more profitable and sustainable production. The objectives of this study were to assess the level of the adoption of improved technologies and management practices and to identify the key factors influencing the adoption of these technologies in the major wheat-producing areas of Sudan. The study identified that access to quality seeds, credit, and extension services were the key determinants of adopting improved wheat production technologies in the irrigated heat-prone environments of Sudan.
2. Materials and Methods
The research methodology included the study areas, survey and data collection design, sample size and sampling of the villages, attribution, quality of data, analytical techniques, and data analyses.
2.1. Study Areas
The study areas included three major wheat-producing areas in Sudan: Gezira, Northern, and Kassala states (
Figure 1). The Gezira Scheme in Gezira state is the largest area of wheat in Sudan (about 155,000 ha in season 2020/2021). The Gezira Scheme consists of two major areas: the Gezira area and the Managil extension. The Gezira area is about 0.504 million hectares lying contiguous to the Blue Nile, and the Managil extension, developed between the years 1957 and 1962, covers an area of about 0.370 million hectares lying to the southwest of the Gezira Scheme [
24]. The study areas also included Northern state, which represents the traditional wheat area in Sudan, and the New Halfa Agricultural Corporation in Kassala state as one of the important wheat areas in Sudan, which was established in the early 1960s to accommodate the displaced people from Wadi Halfa area in Northern state. Together, the three areas represented more than 80% of the total wheat area in Sudan during 2019/20–2020/21 [
25].
2.2. Design of the Survey and Data Collection
The wheat production technologies and the prospects of farmers’ activities for wheat were considered to determine the sources of production inefficiency in the targeted areas and, finally, to identify and prioritize the constraints for optimal use of resources. A questionnaire was designed, data were collected, analytical tools were selected, threats to sustainable production were considered, and an outline for the strategy, design, and implementation of long-term research activities was provided. Rapid Rural Appraisal (RRA) was conducted by visiting the selected sites to assess the situation and collect the basic relevant information related to wheat production. Based on RRA results and considering the objectives of this study, a comprehensive questionnaire has been designed for data collection. The questionnaire included almost all the main variables needed to investigate wheat production practices and efficiency. A multistage stratified random sampling technique was used to collect the primary data from various wheat producers.
2.3. Sample Size and Areas
Two major considerations influence the choice of any sampling design: the desire to avoid bias in the selection procedures and to achieve the maximum precision for a given outlay of resources to achieve the stated objectives. The sample size has been determined according to the variation in the target populations. A purposive random sampling technique was used. Various farmer groups were interviewed, representing 300 wheat farmers from the three selected areas (
Table 1). Respondent farmers within these areas have been selected and interviewed. The farm survey was conducted in 2021 under the project “Development of Climate Change Resilient Innovative Technologies for Sustainable Wheat Production in the Dry and Heat Prone Agro-ecologies of Sudan and Sub-Saharan Africa”. The collected data consisted of items related to the cost of production, current production practices at all levels, and the main factors affecting crop production.
2.4. Attribution and Quality of Data
The questionnaire was designed precisely to reflect the objective of the study. Questions were formulated to facilitate the calculation of the required indicators that assess the production and current practices dominantly applied by farmers for wheat cultivation in targeted areas. Well-trained enumerators collected the primary data. The interviews with different stakeholders proved that all respondents had the same information about implementing activities in the same area.
2.5. Analytical Techniques and Data Analysis Tools
Appropriate analytical tools and models were used to derive indicators that reflect the progress and the impact of crop management practices on wheat production and farming systems to measure the success of using improved technologies in the study areas, as reflected by the adoption of best technologies; to mitigate the drastic effect of traditional farming systems; and to improve the household incomes of the rural communities.
The information was appropriately arranged, and the Statistical Package for Social Sciences (SPSS version 20) was used to analyze the collected data. Cross-tabulation with chi-square analysis was used to test the null hypotheses (the row and column variables are unrelated) whenever this hypothesis makes sense for a two-way variable. The columns represented the surveyed areas, whereas rows represented the socioeconomic and production variables.
A paired sample test was used to compare the wheat productivity of farms where improved technologies and management were applied with the farms where improved technologies and management were not applied. The binary logistic model was used to analyze the factors affecting wheat farmers’ perceptions of the improved and recommended technologies.
2.6. Selection of the Model
The logit model was used because the properties of estimation procedures are desirable for evaluating the adoption rate situation [
26]. Binary logistic regression predicts a categorical (usually dichotomous) variable from a set of predictor variables [
27]. The binary logistic regression predicts a dependent variable with only two outcomes where regular linear regression models (whether simple or multiple) are inappropriate [
28]. It was adopted here to analyze the factors affecting the wheat farmers’ perceptions towards the improved and recommended technologies. The dependent variable in this model was represented by a dummy variable that reflects whether the wheat farmers are adopters of improved technologies or non-adopters. The independent variables included the farmers’ socioeconomic characteristics and wheat production factors. The socioeconomic characteristics were wheat farming experience, education level, wheat area, and land tenure, whereas production factors included access to quality seeds, access to credit, and access to extension services. The coefficient values measure the expected change in the logit for a unit change in the corresponding independent variable, other independent variables being equal [
29]. The coefficient sign indicates the direction of the variable effect on the logit. A positive value indicates an increase in the likelihood that a farmer will change from the baseline group to the alternative option [
29]. SPSS software 20 was used to analyze the data. The general logistic model uses the following equation:
where
= Response probabilities to be modeled;
= Intercept parameter;
= Vector of slope parameters;
= Vector of explanatory variables.
Specifically, the logit model in this study takes the following form:
where
Pi = Probability that the ith wheat farmers acknowledge improvement in the production situation after adopting improved technologies;
1 − Pi = Probability that the ith wheat farmers do not acknowledge any improvement in the production after adopting improved technologies;
YR = Wheat farmers’ experience (years);
YE = Wheat farmers’ education level (years);
WA = Wheat area (ha);
LT = Land tenure (Farmers owned their land = 1, otherwise = 0);
SA = Wheat quality seed access (seeds accessible = 1, otherwise =0);
RF = Received finance (received finance for wheat production = 1, otherwise = 0);
EP = Extension services provision (received extension services = 1, otherwise = 0);
Bj = Logit coefficients (j = 0, 1… 12);
Ui = Random disturbances.
2.7. Trade-Off Analyses of Improved Technologies and Management
The most important wheat production technologies and management practices were selected for comparative analyses between farmers who adopted improved technologies and non-adopters.
Table 2 illustrates these technologies and management practices. It is worth noting that a farmer who adopted seven out of nine of these improved technologies and management practices was considered an adopter.
4. Conclusions
A farm survey was conducted among 300 farmers from Northern, Kassala, and Gezira states, representing more than 80% of wheat-producing areas in Sudan. A structured questionnaire covering all recommended technological options for optimum and sustainable wheat production was used. Besides descriptive statistics, a binary logistic model was used to identify the socioeconomic and technical factors influencing farmers’ perceptions of improved and recommended technological options. The study found a wide range of adoption rates depending on the specific technology practice and the area surveyed. The results of the binary logistic regression showed that five of the seven explanatory variables that were considered to influence the perception of wheat farmers on the decision to adopt improved and recommended technologies significantly influenced the decision of farmers to adopt the technologies. In particular, access to quality seeds, financial credit, and extension services were found to be the most critical determinants of the adoption of improved technologies.
Adopting integrated crop management is crucial for sustainable and economically viable crop production in areas under, or expected to be under, stress due to climate change. Improved technological options developed for the dry, heat-stressed environments of Sudan need to be adopted by farmers for sustainable and profitable wheat production. It is expected that the adoption of approaches and methodologies that bring together all stakeholders along the wheat value chain, including policymakers, to jointly identify, analyze, and prioritize challenges and develop and implement solutions and work plans using feedback, reflection, and lesson-learning mechanisms could raise farmers’ awareness and enhance the adoption of improved technologies. By identifying and addressing the barriers to adopting improved technologies, wheat productivity is expected to improve, leading to more cost-effective wheat production and, ultimately, more profitable and sustainable production.