Rural Development and Dynamics of Enhancing Agricultural Productivity in Senegal: Challenges, Opportunities, and Policy Implications

Abstract

Understanding agricultural production dynamics is vital for addressing global food security in the least developed countries. In Senegal, the issues of rural development and enhancing agricultural productivity are still less understood. Using survey data (n = 600) from the Thiès region, this study aims to explore factors that influence agricultural productivity in Senegal. The multinomial probit model is estimated using maximum simulated likelihood (MSL) methods. This approach is necessary due to the presence of multiple-choice categories. The results highlight that young farmers aged 18–30 are less likely to achieve high production (>10 tons) compared to their older counterparts (p < 0.01). In contrast, older farmers (31–60) report higher income stability (p < 0.05). Education levels also impact production, with farmers having lower or upper secondary education being less likely to attain high production than illiterate farmers (p < 0.05). Receiving subsidies reduces the likelihood of high production (p < 0.01). Larger landholdings (>1 ha) correlate with lower production odds (p < 0.01), suggesting diminishing returns. Gender disparities are evident, with male farmers being 45.6% more likely to report income declines (p < 0.1). Marginal effects show that acquiring land through rental or purchase significantly boosts income (p < 0.01), while traditional ploughing increases the sown area (p < 0.01). Policymakers should enhance training in agriculture, improve subsidies, secure land tenure, and promote certified seeds to boost productivity. This study highlights the need for targeted policies on training, subsidies, land tenure, and sustainable practices to enhance Senegal’s agricultural productivity.

1. Introduction

Cultivated land is crucial for human prosperity and is shaped by natural processes, socio-economic factors, and environmental sustainability [1]. Rural development can be understood as the unfolding of capitalism in rural areas [2]. In Senegal, it plays a crucial role in shaping the nation’s agricultural productivity [3]. For instance, smallholder agriculture in South Africa exhibits significant structural variation at both the micro and macro levels [4]. In contrast, smallholder farmers in Senegal, who depend on rain-fed crops, make up over 95% of the agricultural sector and employ 70% of the population [5,6]. Agriculture is central to Senegal’s economy, providing livelihoods to the majority of rural populations. However, despite its potential, productivity growth has been hindered by various factors, such as limited access to resources [7]. Moreover, as noted, the previous rainfall variability and climate-related shocks, like droughts, impact agricultural productivity, leading to land abandonment and livelihood challenges [8,9,10]. Although not included in this study, these factors further complicate the rural development process. Agricultural productivity drives rural development in Senegal, highlighting key challenges, opportunities, and policies to improve food security and economic growth.

Agricultural productivity boosts food availability, while rural development enhances infrastructure, supporting food security and economic growth. In an era of global environmental challenges, understanding the dynamics of food production in Sub-Saharan Africa is crucial for sustainable development, poverty alleviation, and food security [11]. In Nigeria, over 90% of farmers rely on their experiences to adapt to seasonal challenges, implement effective storage practices, and enhance soil fertility [12]. Tackling sustainable agriculture is a critical component of long-term agricultural modernization in Senegal [1]. It emphasizes practices that ensure food security and socio-economic well-being. Agriculture is the backbone of Senegal’s economy, providing jobs for a large number of people [3]. Consequently, despite challenges such as land degradation and limited access to resources, sustainable agricultural practices are crucial to addressing food security and poverty [9,13]. In Senegal, agriculture is not just an economic activity but also a central aspect of cultural identity and community life. Growth in agricultural productivity also contributes to reducing poverty [14,15]. In essence, higher agricultural productivity leads to increased incomes, which can be reinvested into local communities, further enhancing rural development. Within this context, the contributions of farmers are increasingly recognized as pivotal for the achievement of agricultural sustainability. In sum, boosting agricultural productivity in Senegal is key to ensuring food security and supporting rural development to ensure long-term resilience.

Agriculture in the least developed countries does not ensure food security [16]. Despite Senegal’s reliance on agriculture, smallholder farmers face numerous challenges that limit their agricultural productivity. This is reflected in the national income, which represented about US$23.57 billion in 2019 [17]. These challenges include inadequate access to credit, low levels of technological adoption, limited agricultural training, and poor infrastructure [18,19]. In essence, barriers to adopting digital agriculture include farmers’ economic constraints, a lack of infrastructure, technological knowledge, and limited access to digital tools and training opportunities [20]. Accordingly, investing in agricultural technology and better seeds can help boost agricultural productivity [21,22].

Furthermore, in enhancing productivity, food, and nutritional security, adoption remains uneven and limited due to resource constraints, a lack of awareness, and infrastructure challenges [4]. Addressing these issues is essential for improving agricultural productivity, ensuring food security, and fostering sustainable rural development. This research aims to explore the challenges and opportunities within the agricultural sector and identify effective policy measures to enhance agricultural productivity in Senegal. What socio-economic, demographic, and institutional factors significantly influence agricultural productivity among smallholder farmers in the Thiès region of Senegal? What opportunities exist to improve agricultural productivity in Senegal?

Existing literature on rural development and agricultural productivity in Senegal highlights several factors impacting growth, such as access to agricultural inputs and credit systems. Previous studies have primarily focused on natural factors affecting agricultural production in Senegal, including low and irregular rainfall [23,24,25,26]. Additionally, rising temperatures and extreme weather events significantly influence crop yields [27,28,29,30]. Other natural challenges include abiotic stresses, such as climate variability, and biotic stresses like pests and diseases. Studies show that limited access to finance and training is a major barrier to modernizing agricultural practices [3,9].

Additionally, research has identified the importance of crop diversification, soil conservation, and climate-smart agriculture in enhancing productivity. The literature also emphasizes the role of government policies and international partnerships in supporting rural development. For instance, in Northwest Cambodia, 92% of respondents reported reductions in household income due to economic challenges and environmental impacts [19]. However, gaps remain in addressing the specific dynamics of agricultural productivity within the context of Senegal’s unique socio-economic and environmental challenges.

Given the critical role of agriculture in Senegal’s economy and the challenges faced by smallholder farmers, understanding the dynamics of agricultural productivity enhancement is essential for achieving rural development and food security. This study aims to fill the gap in the literature by exploring the factors influencing agricultural productivity in Senegal, particularly in the Thiès region. By employing a Multinomial Probit (MNP) model, this research seeks to identify the key challenges and opportunities for improving agricultural productivity and income stability among farmers. The findings will provide valuable insights for policymakers and development practitioners, guiding the design of targeted interventions that address the specific needs of rural communities in Senegal.

2. Materials and Methods

2.1. Study Area and Data Collection

The Thiès region, situated between latitudes 10°44′46″ N and 10°52′46″ N and longitudes 78°39′11″ W and 78°44′13″ W, spans approximately 6669.6 km² [8]. As of 2020, the Thiès region in Senegal had a population of 2,162,831, according to the National Agency of Statistics and Demography (ANSD). The region is renowned for its agricultural activity, particularly in the production of essential crops, such as groundnuts, maize, millet, sorghum, and cowpea. In 2020, these crops were cultivated on approximately 266,668.24 hectares, yielding a total of 253,784.08 tons. Notably, groundnuts and millet alone accounted for a substantial 78.1% of the total crop production. In addition to its prominence in staple crops, the Thiès region is the second-largest producer of vegetables in Senegal, contributing one-third of the country’s vegetable cultivation and approximately 30.25% of its total vegetable output. Given its significant agricultural contribution, especially in groundnuts and vegetables, Thiès plays a crucial role in Senegal’s agricultural economy, supporting food security and providing livelihoods for many families in the region. As shown in Figure 1d, the topography is flat, with a maximum altitude of 141 m.

2.2. Survey Design and Sampling

Surveys and questionnaires are frequently employed in social science research to collect data and examine patterns [13]. A social survey questionnaire was created to gather data from farmers in 11 out of 31 communes in the Thiès region, based on population size and agricultural significance. The survey process began with interviews of district authorities and involved a two-stage selection process. In the first stage, districts were chosen to represent regional variations in agriculture, land use, and socio-economic conditions. Villages were then randomly selected from the 2013 Senegal census based on population size. In the second stage, 15 farmers were randomly chosen from each village, with a total of about 40 villages included. In Senegal, local authorities play a key role in survey data collection. Village chiefs help conduct interviews, and the selection of participants is based on farm size, with local authorities identifying the respondents. The survey had four sections, with this study using the section on agricultural productivity dynamics in Senegal’s Thiès region, covering production, income stability, land use, and socio-economic factors.

A total of 600 responses were collected in October 2022. In addition to the survey, interviews with local government leaders were conducted to provide further context. The questionnaire consisted of four sections, focusing on farmers’ views on cultivated land protection. Data collection was performed using CommCare HQ platform through face-to-face interviews, ensuring that ethical considerations were met, including the handling of sensitive data, like farm revenue. After filtering out 15 incomplete questionnaires, 585 valid responses were used for the analysis. The reliability of the questionnaire was assessed using the Kaiser–Meyer–Olkin (KMO) measure, with values above 0.5 indicating suitability for factor analysis [31,32]. The KMO test showed values above 0.6, and Bartlett’s test yielded a p-value of 0, confirming the questionnaire’s high reliability and strong construct validity.

2.3. Method

2.3.1. Research Farmwork and Description of the Selected Variables

(a) Dependent Variables: The socio-economic and agricultural practices of the farmers in the study present a profile predominantly characterized by small-to-medium-scale operations (

Table 1. Description of the selected variables.

Variables	Descriptive	Mean	Std. Dev.	Min	Max
Dependent Variables
Agricultural production	Total agricultural harvest (Tons): 1 = less than 1 ton; 2 = 1–5 tons; 3 = 5–10 tons; 4 = +10 tons	1.942	1.158	1	4
Agricultural income	Farmers income status: 1 = increase, 2 = decreasing, 3 = fluctuating	2.089	0.543	1	3
Farmer-sown land area evolution	Sown area status: 1 = increase, 2 = decreasing, 3 = fluctuating	2.13	0.473	1	3
Independent Variables
Sex	Gender: 1 = male; 0 = Female	0.72	0.449	0	1
Age of the farmers	Age: 1 = {18–30} years, 2 = {31–40} years, 3 = {41–50} years, 4 = {51–60} years, 5 = +60 years	3.129	1.472	1	5
Education	Education level: 0 = illiterate, 1 = primary, 2 = lower secondary education, 3 = upper secondary education; 4 = university	1.029	1.302	0	4
Farmer experience in agricultural	Experience in agriculture: 1 = less than 10 years; 2 = {10 to 20}; 3 = {20 to 30}; 3 = {30 to 40}; 4 = + 40	3.077	1.456	1	5
Farmer training in agriculture	If farmers have received training in agricultural; yes = 1, no = 0	0.07	0.256	0	1
Farmer using fertilizer in agricultural	Fertilizer use: 1 = yes, 2 = no	0.595	0.491	0	1
Farmer subsidy status	If farmers have received agricultural subsidies yes = 1, no = 0	0.156	0.363	0	1
Farmer means agricultural investment	The main agricultural expense: 1 = seed; 2 = fertilizer; 3 = pesticide; 4 = hire of materials; 5 = hire of land; 6 = labor force; 7 = other	2.05	1.896	1	7
Farmer agricultural financial system	The farmer’s farming financial system: 1 = family; 2 = by itself; 3 = agro-business; 4 = cooperative	1.763	1.199	1	4
Farmer agricultural material	Material: 1 = only old material; 2 = modern and old material	1.273	0.446	1	2
Farmer agricultural labor force	Persons in the house working as labor force in the farming sector: 1 = {1–3} persons; 2 = {3–6} persons; 3 = {6–9} persons; 4 = {9–12} persons; 5 = {12–15} persons; and 6 = {+15} persons	1.978	1.051	1	6
Farmer’s total sown land area	The farmers sown land area (Hectare): less than 1 ha = 1; 2 = {1–3} ha; 3 = {3–5} ha; 4 = {5–7} ha; 5 = {+7} ha	2.144	1.231	1	5
Cultivated land property	Farmers’ mode of acquisition of farmland. 1 = inheritance; 2 = rental; 3 = loan; 4 = purchase; 5 = other	1.262	0.759	1	5
Farmer-cultivated land protection strategy	Farmers practiced one of these methods: 1 = land registration; 2 = obtain title deed; 3 = secure land by fencing; 4 = orchard farming; 5 = no strategy	2.123	1.297	1	5
Cultivated land quality status	Perceptions of farmers on the quality of cultivated land. 1 = very high; 2 = high; 3 = moderate; 4 = poor	1.966	0.818	1	4
Seed quality	The quality of the seed: 1 = certified, 2 = not certified, 3 = do not know	1.576	0.637	1	3
Seed origin	What is the origin of the seed: 1 = purchase; 2 = personal reserve; 3 = subsidies	1.779	0.544	1	3
Crop rotation practices	Whether farmers practice crop rotation or not: yes = 1; no = 0	0.451	0.498	0	1
Traditional plough	Whether farmers practice traditional ploughing or not, yes = 1; no = 0	0.714	0.452	0	1
Awareness of land policies	If farmers are aware of the law regarding protected farmland: yes = 1; no = 0	0.482	0.5	0	1
Improve cultivated land quality	If farmers have received help to fight against cultivated land degradation, yes = 1; no = 0	0.067	0.25	0	1

). As a cornerstone of rural development, agricultural output plays a crucial role in food security, economic growth, and livelihoods in agrarian economies [33]. In our sample, the average total agricultural production is approximately 1.94 tons, which reflects the predominance of small-to-medium-scale farmers, with income levels aligned with production volumes.

In developing countries, shifts in the cultivated area are often indicative of changes in agricultural production [13,34,35]. In this context, the average sown area in our sample is 2.13 hectares, suggesting a shift from horizontal land expansion to vertical farming practices. This trend may stem from resource constraints or environmental challenges. These changes are closely linked to farmers’ income levels, which, in turn, influence their ability to reinvest in their farms, adopt sustainable practices, and break the cycle of poverty—key elements of rural development [36].

Farm viability is typically measured by the farm’s ability to meet the income needs of the family while covering operational costs [37]. In essence, stable or increasing farm income is vital for ensuring sustainability and adaptability to shocks, such as climate variability. Conversely, declining income points to systemic vulnerabilities that may undermine agricultural resilience [38,39].

Food security and agricultural production are deeply influenced by a variety of factors, including natural, geographic, economic, historical, and political elements [16]. In light of this, this study selected agricultural production, income, and the evolution of sown land area as dependent variables. These variables serve as critical indicators to measure the dynamics of agricultural productivity enhancement in Senegal, reflecting how shifts in farming practices can influence overall farm viability, income stability, and the capacity to respond to external challenges.

(b) Independent Variables: In the context of rural development in Senegal, exploring the relationship between farmer characteristics and agricultural investment is integral to understanding farmers’ capacity to boost productivity. The sex and age of farmers influence their approach to farming, as gender roles and age can impact decision-making, labor allocation, and adoption of modern practices [40,41]. In our sample, there are differences in gender. A key issue is gender disparities in time allocation [42]; 72% of respondents are male, with an average age of 41–50, indicating an aging farming population. Meanwhile, women play a crucial role in Africa’s economic development, particularly in agriculture, where their involvement is significant [43]. Farmers’ education and experience in agriculture are crucial for knowledge application, affecting how farmers adapt to technological advancements and modern farming methods [44,45]. Although many farmers have 20–30 years of experience, only 7% have received training, and just 15.6% have benefited from subsidies. Farmer training further enhances their ability to implement improved practices, while fertilizer use demonstrates the adoption of inputs that can directly increase productivity [9,46]. We note that about 59.5% of farmers apply fertilizers, with fertilizer being the largest expenditure. In contrast, the survey data indicate that most farmers have completed only primary school, which may hinder the adoption of modern agricultural practices. The subsidy status indicates the financial support available to farmers, impacting their ability to invest in better farming inputs.

Farmers’ diversification responses to climate shocks vary and are influenced by resources, knowledge, and circumstances [47,48]. In this context, suitable agricultural finance and the use of good seeds are necessary in agriculture and the context of climate change. Agricultural finance plays a pivotal role in facilitating the modernization and commercialization of farming practices and bolstering global food security [49]. Hence, in the context of our study, mean agricultural investment can show the extent to which farmers are financially committed to improving productivity. Most farmers rely on self-financing or family labor, with minimal participation in cooperatives or agro-businesses. In developing countries, smallholder farmers often lack long-lasting sources of credit, and the agricultural financial system plays a crucial role in influencing their access to necessary resources by reflecting the availability of credit and financing options [50,51]. Material use and the agricultural labor force describe the resources at the farmers’ disposal, which affect output levels. Hence, due to traditional production techniques, livestock farmers face the problem of low productivity [52]. The sown land area and cultivated land property relate to land availability and ownership, influencing investment decisions. In essence, land tenure security plays a crucial role in driving success in sustainable agriculture and ensuring food security [53].

Labor is mainly family-based, with most farms consisting of 3–6 members. Farms are typically small, averaging 1–3 hectares, and land is mostly inherited, which limits access to credit or legal protection. Sustainable soil and land use are pivotal for achieving multiple Sustainable Development Goals, including poverty alleviation and climate resilience [4,54,55]. Land protection strategies and quality directly relate to long-term productivity, as sustainable practices maintain land health [56]. Practices like crop rotation and the traditional plough help maintain soil fertility, while seed quality and its origin impact crop yields [57,58,59]. Few farmers engage in land-protection measures or crop rotation, and many still use traditional ploughing methods. Therefore, it is crucial to identify and develop sustainable land-management practices to address widespread resource degradation from poor land use [60]. Farmers’ awareness of land policies plays a critical role in adhering to regulations and improving land use [1,61]. Together, these factors shape the opportunities and challenges of increasing agricultural productivity in Senegal, pointing to the need for a coordinated approach to policy, training, and financial support.

2.3.2. Model Construction

The multinomial probit (MNP) formulation offers a flexible framework that accounts for interdependent alternatives in discrete choice analysis, enabling more accurate modeling of decision-making processes [62]. This study employs the MNP model to analyze three categorical outcomes in Senegalese agriculture: total agricultural production level, farmers’ income status, and sown area dynamics. Below is the formal econometric framework:

(1)

The model specification

For each dependent variable, the MNP model is structured as follows:

• Latent Utility Equations: For farmer i and outcome category j, the latent utility Uij is as follows:

U_ij = X_iβ_j + ϵ_ij, j = 1,2,…,J

(1)

where U_ij is the latent utility of farmer i for outcome j; X_i is the vector of observed explanatory variables (e.g., gender, age, education); β_j is the vector of coefficients specific to category j; and ϵ_ij is the random error term, assumed to follow a multivariate normal distribution.

ϵi = (ϵ_i1,ϵ_i2,…,ϵ_iJ)∼N(0,Σ)

(2)

The covariance matrix Σ captures correlations between utilities across categories. For identification, variances are normalized (e.g., Σ₁₁ = 1).

b.

Observed Outcome: The observed categorical outcome Yi corresponds to the category with the highest utility:

$$Y_{i=arg{\displaystyle \frac{max}{j}}} U_{IJ}$$

(3)

c.

Probability Expression: The probability that farmer i selects category j is

$$P(Yi={\displaystyle \frac{j}{Xi}})={{\displaystyle \int }}_{-\infty }^{+\infty }1\left[Uij>Uik\forall k\not\approx j\right]f\left(єi\right)dєi$$

(4)

where f(ϵi) is the multivariate normal density function.

(2)

Estimation Strategy

The model is estimated via maximum simulated likelihood (MSL) due to the high-dimensional integration required for probabilities. This study employs a multivariate probit (MVP) model to investigate the joint determinants of three interrelated outcomes in the context of smallholder agriculture: total agricultural production, farmers’ income status, and sown area dynamics. The choice of this model is motivated by the potential interdependence of these categorical outcomes, which a similar set of socio-demographic, institutional, and agronomic factors could influence. To justify this model choice, it is important to note that traditional univariate probit or multinomial models treat outcome equations independently and ignore the potential correlation between unobserved factors influencing each outcome. The log-likelihood function is

$$L\left(\beta , \sum \right)={{\displaystyle \sum }}_{i=0}^N\log ({{\displaystyle \sum }}_{J=1}^{J}1\left[Y_I=J\right], P(y_1={\displaystyle \frac{j}{X_i}}))$$

(5)

Simulation techniques (e.g., Geweke–Hajivassiliou–Keane draws) approximate the integrals [63]. Robust standard errors address heteroskedasticity.

✓

Dependent Variables: Three categorical outcomes are modeled separately:

(a)

Production Level (J = 4): 1 = <1 ton; 2 = 1–5 tons; 3 = 5–10 tons; 4 = >10 tons (reference).

(b)

Income Status (J = 3) 1 = increasing; 2 = decreasing; 3 = fluctuating (reference).

(c)

Sown Area Status (J = 3) = 1: increasing; 2 = decreasing; 3 = fluctuating (reference).

✓

Independent Variables: Explanatory variables include gender, age groups, education levels, experience, training, fertilizer use, subsidies, etc. All categorical variables are dummy-coded with explicit reference categories (e.g., female for gender, illiterate for education).

(3)

Marginal Effects

Post-estimation, marginal effects quantify the change in predicted probabilities for outcome j due to a unit change in X_k:

$${\displaystyle \frac{\partial P\left(Y_i=j/X_i\right)}{\partial X_K}}={\beta }_{jk }\varnothing \left(X_i{b}_j\right)-{{\displaystyle \sum }}_{m\not\approx j}{\beta }_{mk}·\varnothing \left(X_i{b}_m\right)$$

(6)

where ϕ (⋅) is the standard normal density function.

(4)

Model Justification

The multinomial probit (MNP) model can be viewed as a special case of a general utility maximization model [64]. The MNP is preferred over the multinomial logit (MNL) because it relaxes the assumption of independence of irrelevant alternatives (IIA). This is critical here, as unobserved factors (e.g., soil quality perceptions) likely correlate across agricultural outcomes.

3. Results

3.1. Total Agricultural Production (Reference: More than 10 Tons)

The analysis reveals several factors that negatively impact farmers’ ability to achieve high agricultural production. Respondents aged 31–40 show consistently negative coefficients across all production categories, indicating a significantly lower agricultural output compared to younger farmers. Meanwhile, those aged 41–50 and 51–60 exhibit a mix of both negative and positive coefficients. Interestingly, farmers without formal education are more likely to be in the highest production category than those with secondary education, suggesting practical knowledge may be more beneficial than formal schooling.

Experience alone does not guarantee high production; farmers with 10–20 years of experience are often in lower production brackets, indicating the need for technology and effective input use. Government subsidies appear to reduce production, as farmers receiving them are less likely to produce over 10 tons. Spending on seeds correlates with higher production, while spending on fertilizers or other items results in lower output.

Larger land sizes (over 1 ha) paradoxically correlate with lower production, possibly due to diminishing returns or poor land management. Farmers who perceive their land as “poor” show significantly lower production. Additionally, farmers using government-provided seeds, practicing crop rotation, or aware of land protection laws are less likely to report high production.

Women, often underrepresented in agricultural decision-making, may face additional challenges, such as limited access to resources, education, and subsidies, further hindering their ability to contribute effectively to agricultural production. Addressing these gender imbalances could improve productivity.

3.1.1. Farmers’ Income Status (Reference: Fluctuating)

The model indicates that age significantly influences the income status, with farmers aged between 31 and 60 more likely to report higher income than their younger counterparts. This suggests that older farmers benefit from sophisticated knowledge, social capital, or accumulated wealth, enabling them to maintain or increase their income. However, education, agricultural training, and fertilizer use do not appear to affect income variation significantly.

Agricultural expenses play a crucial role, as farmers whose primary costs are fertilizers or labor are more likely to report income decreases, suggesting inefficiencies in these inputs. On the economic side, land acquisition methods like rental or “Other” methods enhance income growth, likely due to access to better-quality or more fertile land.

Farmers working on “poor” land report significantly higher income, which could be attributed to financial aid or resilience mechanisms that allow farmers to thrive despite harsh conditions. Larger sown areas (>7 ha) and secure land acquisition methods, such as purchase, also contribute positively to income growth.

The role of women is crucial here. Women often face barriers in accessing land, financial resources, and agricultural training, which can limit their ability to benefit from income-enhancing factors, such as land tenure security. Addressing gender inequalities in land ownership, access to capital, and training could significantly improve women’s income prospects in agriculture.

3.1.2. Sown Area Status (Reference: Fluctuating)

The analysis reveals several factors influencing the sown area of farms. Age plays a significant role, with farmers over 60 more likely to report a decrease in the sown area, potentially due to physical limitations or land access issues. Investment patterns also affect land expansion, as farmers spending primarily on labor, fertilizer, or non-seed inputs are less likely to increase their sown area, possibly due to reduced capital for land acquisition or maintenance.

Financial systems are a key factor, as farmers relying on agro-business models are less likely to expand their sown area, likely due to contractual constraints or land leasing limitations. Interestingly, land sizes above 5 ha are associated with a decreased likelihood of further expansion, suggesting a ceiling effect for larger plots. Loan-based land access is similarly linked to a reduced chance of expanding the sown area, possibly due to repayment obligations that limit reinvestment.

Farmers using traditional plowing methods, however, are more likely to increase their sown area, indicating that these techniques may be more cost-effective or culturally ingrained. The awareness of protected land laws and the use of government-provided seeds negatively correlate with changes in sown areas, potentially due to regulatory constraints.

For women farmers, barriers to land access, a lack of financial resources, and limited autonomy in agro-business systems can further constrain their ability to expand the sown area. Addressing these challenges can significantly enhance women’s involvement in agricultural growth and land expansion.

This multinomial probit analysis shows how gender, age, education, and inputs, together with land access and institutional factors, influence the productivity, income, and land use of farmers (

Table 2. Summary of the main regression results.

	Dependent Variables
	Agricultural Production (Ref.: >10 Tons)			Farmer’s Income Status (Ref.: Fluctuating)		Sown Area Status (Ref.: Fluctuating)
Items	<1 Ton	1 to 5	5 to 10	Increase	Decrease	Increase	Decrease
Sex (Ref.: Female)
Male	0.185	−0.069	−0.276	0.364	0.456 *	−0.504	0.182
Age (Ref.: 18–30 years)
31–40 years	−3.553 ***	−2.665 **	−2.641 ***	1.313 ***	0.715 **	1.415	0.007
41–50 years	1.043	0.695	−0.152	1.242 **	0.476	1.602	0.775
51–60 years	−1.063	−1.527	−2.659 **	1.448 **	0.940 *	1.754	0.598
>60 years	−1.772	−1.942	−3.359 **	1.573 *	0.935	0.331	0.487
Education (Ref.: Illiterate)
Lower secondary	−2.464 **	−2.368 **	−3.891 ***	−0.882 *	−0.318	−0.323	0.408
Upper secondary	−3.962 ***	−2.881 **	−3.744 ***	0.172	−0.285	−0.783	−0.517
University	0.059	0.174	−1.622 *	−0.252	0.032	−0.297	0.597
Agri-Experience (Ref.: less than 10 years)
10 to 20	3.609 **	3.753 ***	3.070 **	−0.644	−0.657 *	−0.821	−0.327
20 to 30	1.838	1.460	1.290	−1.055 *	−0.742	−1.463	−0.011
30 to 40	1.497	3.261*	2.648 *	−0.779	0.009	−0.074	0.007
More than 40	1.860	2.462	1.547	−1.950 **	−0.746	−0.875	−0.321
Subsidy (Ref.: No)
Yes	−3.157 ***	−1.826 *	−1.922 *	0.579	0.371	0.537	0.063
The main agricultural expense (Ref.: Seed)
Fertilizer	−2.188 *	−0.174	0.178	−0.773	−1.193 ***	−1.954 *	−1.393 ***
Hire of materials	3.673 *	3.799 *	5.083 **	−0.214	−0.530	−2.179	−0.891
Hire of land	21.720	20.164	−20.204	−2.155	−2.094 **	−30.654	−0.863
Labor force	−1.694	−0.970	−0.681	−0.766	−1.234 ***	−3.088 ***	−0.668 *
Other	−9.178 ***	−7.302 ***	−41.418	−0.469	−0.210	3.116**	0.365
The farmer’s farming financial system (Ref.: Family)
By itself	−2.094	−2.970**	−2.361 *	0.456	−0.174	0.764	−0.535
Agro-business	−2.381	−2.306	−35.448	−0.944	−1.133	−28.360	−2.546 ***
Cooperative	−1.715 **	−3.197 ***	−2.196 ***	−0.532	−0.164	−0.260	−0.127
Materiel (Ref.: Only old materiel)
Modern and old materials	−4.369 ***	−0.692	−0.166	1.135 ***	−0.094	−0.201	0.047
Persons in the house working as labor force in the farming sector (Ref.: 1–3 persons)
3–6 persons	−0.021	−0.173	−0.980	0.089	−0.197	−0.467	−0.628 **
6–9 persons	−0.422	0.545	−0.526	0.605	−0.698 **	2.086 **	−1.028 ***
9–12 persons	−2.002	−2.224	−3.549 **	−0.138	−1.164 **	0.396	−1.345 ***
12–15 persons	5.090 **	2.896	−35.566	0.650	−1.101	−1.051	−1.391
15+ persons	−1.819	−56.090	−1.652	1.905*	−1.720 *	4.040 **	−2.708 ***
The farmers’ sown land area (Hectare) (Ref.: Less than 1 ha)
1–3 ha	−17.544 ***	−15.893 ***	−13.927 ***	−0.202	−0.601 *	1.149	−0.466
3–5 ha	−19.746 ***	−18.223 ***	−15.707 ***	0.211	−0.518	0.706	−0.512
5–7 ha	−21.609	−19.765 ***	−15.830 ***	−0.568	−0.764	0.871	−1.750 ***
+7 ha	−27.423 ***	−25.118 ***	−17.343 ***	0.895	−1.235 **	3.827 ***	−1.374 ***
Farmers’ mode of acquisition of farmland (Ref.: Inheritance)
Rental	16.848 ***	18.208	−19.011	1.667 **	−0.638	2.493 *	−0.877
Loan	2.567 *	1.783	1.438	0.191	−0.663	0.090	−0.995 **
Purchase	0.388	1.523	0.829	−0.516	−0.402	3.147 *	−1.052
Other	22.235 ***	22.857	−15.759	11.411 ***	10.576	−7.524	27.429
Strategy protects cultivated land (Ref.: Land registration)
Get a title deed	−0.144	−0.190	−0.135	−0.271	−0.333	−1.582 *	−0.482
Secure by fencing	−1.898 **	−1.384 **	−0.687	0.244	−0.034	−1.997 **	−0.423
Orchard farming	4.757 *	4.973 *	4.110 *	−0.512	0.702	−0.742	−0.843
No strategy	−0.176	0.920	0.868	0.933	1.019 **	0.328	−0.354
Perceptions of farmers about the quality of cultivated land (Ref.: Very high)
Moderate	0.788	0.872	1.432 *	−0.796 *	0.528 *	−2.241 *	0.291
Poor	16.713 ***	13.469	−21.444	11.231 ***	10.832	−24.610	−1.760 **
Quality’s seed (Ref.: Certified)
Do not know	9.444	9.826 ***	9.566 ***	1.003	0.065	2.913 **	0.821
Seed’s origin (Ref.: Purchase)
Own reserve	−0.251	−2.026 *	−0.206	−0.133	−0.012	−2.207 **	−0.234
Subsidy	−8.012 ***	−6.025 ***	−4.660 ***	−25.126	−1.326 **	−32.598	−1.149 **
Whether farmers practice crop rotation or not (Ref.: No)
Yes	−1.753 **	−1.316 *	−0.404	−0.086	0.150	0.674	−0.460
Whether farmers practice traditional ploughing or not (Ref.: No)
Yes	−2.693	−1.378	10.993	−0.360	−0.491	11.933 ***	−0.841 *
If farmers are aware of the law regarding protected farmland (Ref.: No)
Yes	−3.982 ***	−4.334 ***	−3.319 ***	−0.388	−0.452	0.795	−0.383
Constant	31.520 ***	27.816 ***	−4.023	−0.619	2.737 ***	−13.734	3.961 ***
Observations	583	583	583	583	583	583	583

Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

). Also, the analysis emphasizes that having more land, more experience, or subsidized aid does not guarantee positive outcomes. Rather, effectiveness seems to depend on context-specific combinations of resources, capabilities, and policy frameworks. These analysis outcomes, for policymakers and development practitioners, bring the possibility of tailored approaches to targeted interventions beyond universal support frameworks by focusing on the various farmer types and the constraints and opportunities within their specific contexts (

Table A1. Various farmer types and the constraints and opportunities within their specific contexts.

	Dependent Variables
	Agricultural Production (Ref.: More than 10 Tons)			Farmer’s Income Status (Ref.: Fluctuating)		Sown Area Status (Ref.: Fluctuating)
Items	Less than 1 Ton	1 to 5 Tons	5 to 10 Tons	Increase	Decrease	Increase	Decrease
Sex (Ref.: Female)
Male	0.185	−0.069	−0.276	0.364	0.456 *	−0.504	0.182
	(0.741)	(0.681)	(0.591)	(0.332)	(0.237)	(0.681)	(0.262)
Age (Ref.: 18–30 years)
31–40 years	−3.553 ***	−2.665 **	−2.641 ***	1.313 ***	0.715 **	1.415	0.007
	(1.204)	(1.106)	(0.964)	(0.503)	(0.361)	(0.946)	(0.373)
41–50 years	1.043	0.695	−0.152	1.242 **	0.476	1.602	0.775
	(1.413)	(1.322)	(1.147)	(0.577)	(0.412)	(1.162)	(0.483)
51–60 years	−1.063	−1.527	−2.659 **	1.448 **	0.940 *	1.754	0.598
	(1.618)	(1.490)	(1.288)	(0.698)	(0.509)	(1.414)	(0.549)
>60 years	−1.772	−1.942	−3.359 **	1.573 *	0.935	0.331	0.487
	(1.782)	(1.638)	(1.457)	(0.814)	(0.581)	(1.771)	(0.611)
Education (Ref.: Illiterate)
Primary	0.093	0.866	−1.127	−0.419	0.360	−0.719	0.440
	(0.864)	(0.790)	(0.697)	(0.449)	(0.305)	(0.787)	(0.334)
Lower secondary	−2.464 **	−2.368 **	−3.891 ***	−0.882 *	−0.318	−0.323	0.408
	(1.187)	(1.124)	(1.048)	(0.508)	(0.354)	(0.988)	(0.408)
Upper secondary	−3.962 ***	−2.881 **	−3.744 ***	0.172	−0.285	−0.783	−0.517
	(1.408)	(1.293)	(1.155)	(0.550)	(0.425)	(1.188)	(0.434)
University	0.059	0.174	−1.622 *	−0.252	0.032	−0.297	0.597
	(1.352)	(1.244)	(0.967)	(0.573)	(0.421)	(0.918)	(0.488)
Agri-Experience (Ref.: less than 10 years)
10 to 20	3.609 **	3.753 ***	3.070 **	−0.644	−0.657 *	−0.821	−0.327
	(1.413)	(1.339)	(1.223)	(0.506)	(0.382)	(0.926)	(0.416)
20 to 30	1.838	1.460	1.290	−1.055 *	−0.742	−1.463	−0.011
	(1.625)	(1.536)	(1.354)	(0.641)	(0.483)	(1.242)	(0.523)
30 to 40	1.497	3.261 *	2.648 *	−0.779	0.009	−0.074	0.007
	(1.922)	(1.784)	(1.539)	(0.798)	(0.616)	(1.486)	(0.641)
More than 40	1.860	2.462	1.547	−1.950 **	−0.746	−0.875	−0.321
	(2.003)	(1.855)	(1.642)	(0.842)	(0.604)	(1.686)	(0.635)
Training in agriculture (Ref.: No)
Yes	−1.323	−1.471	−0.701	0.222	0.276	−1.730	0.426
	(1.311)	(1.226)	(0.968)	(0.647)	(0.457)	(1.354)	(0.493)
Fertilizer use (Ref.: No)
Yes	−1.678	−0.274	15.303	−0.167	−0.438	0.891	0.275
	(1.506)	(1.483)	(0.000)	(0.437)	(0.321)	(0.857)	(0.359)
Subsidy (Ref.: No)
Yes	−3.157 ***	−1.826 *	−1.922 *	0.579	0.371	0.537	0.063
	(1.060)	(1.005)	(0.988)	(0.521)	(0.371)	(0.794)	(0.409)
The main agricultural expense (Ref.: Seed)
Fertilizer	−2.188 *	−0.174	0.178	−0.773	−1.193 ***	−1.954 *	−1.393 ***
	(1.210)	(1.090)	(1.010)	(0.541)	(0.361)	(1.025)	(0.388)
Pesticide	24.643	52.982	−7.911	−25.442	0.072	−27.712	0.208
	(0.000)	(0.000)	(0.000)	(0.000)	(1.302)	(0.000)	(1.341)
Hire of materials	3.673 *	3.799 *	5.083 **	−0.214	−0.530	−2.179	−0.891
	(2.154)	(2.047)	(2.132)	(0.694)	(0.528)	(1.563)	(0.576)
Hire of land	21.720	20.164	−20.204	−2.155	−2.094 **	−30.654	−0.863
	(17,047.332)	(17,047.332)	(0.000)	(1.373)	(1.007)	(0.000)	(0.970)
Labor force	−1.694	−0.970	−0.681	−0.766	−1.234 ***	−3.088 ***	−0.668 *
	(1.221)	(1.079)	(0.930)	(0.496)	(0.384)	(0.983)	(0.396)
Other	−9.178 ***	−7.302 ***	−41.418	−0.469	−0.210	3.116 **	0.365
	(2.148)	(2.064)	(0.000)	(0.882)	(0.508)	(1.583)	(0.771)
The farmer’s farming financial system (Ref.: Family)
By itself	−2.094	−2.970 **	−2.361 *	0.456	−0.174	0.764	−0.535
	(1.298)	(1.246)	(1.296)	(0.442)	(0.369)	(0.729)	(0.374)
Agro-business	−2.381	−2.306	−35.448	−0.944	−1.133	−28.360	−2.546 ***
	(11.909)	(11.875)	(0.000)	(1.263)	(0.838)	(0.000)	(0.889)
Cooperative	−1.715 **	−3.197 ***	−2.196 ***	−0.532	−0.164	−0.260	−0.127
	(0.871)	(0.797)	(0.706)	(0.450)	(0.267)	(0.762)	(0.294)
Materiel (Ref.: Only old materiel)
Modern and old materials	−4.369 ***	−0.692	−0.166	1.135 ***	−0.094	−0.201	0.047
	(1.274)	(1.155)	(1.118)	(0.436)	(0.343)	(0.836)	(0.389)
Persons in the house working as labor force in the farming sector (Ref.: 1–3 persons)
3–6 persons	−0.021	−0.173	−0.980	0.089	−0.197	−0.467	−0.628 **
	(0.764)	(0.688)	(0.625)	(0.411)	(0.266)	(0.875)	(0.293)
6–9 persons	−0.422	0.545	−0.526	0.605	−0.698 **	2.086 **	−1.028 ***
	(0.991)	(0.874)	(0.761)	(0.493)	(0.347)	(1.051)	(0.381)
9–12 persons	−2.002	−2.224	−3.549 **	−0.138	−1.164 **	0.396	−1.345 ***
	(1.554)	(1.449)	(1.443)	(0.596)	(0.469)	(1.088)	(0.486)
12–15 persons	5.090 **	2.896	−35.566	0.650	−1.101	−1.051	−1.391
	(2.521)	(2.075)	(0.000)	(0.995)	(0.974)	(2.222)	(0.931)
15+ persons	−1.819	−56.090	−1.652	1.905*	−1.720 *	4.040 **	−2.708 ***
	(2.583)	(0.000)	(1.448)	(1.012)	(0.911)	(2.012)	(0.979)
The farmers’ sown land area (Hectare) (Ref.: Less than 1 ha)
1–3 ha	−17.544 ***	−15.893 ***	−13.927 ***	−0.202	−0.601 *	1.149	−0.466
	(1.487)	(1.512)	(1.637)	(0.434)	(0.307)	(0.901)	(0.352)
3–5 ha	−19.746 ***	−18.223 ***	−15.707 ***	0.211	−0.518	0.706	−0.512
	(1.518)	(1.522)	(1.587)	(0.550)	(0.393)	(1.202)	(0.425)
5–7 ha	−21.609	−19.765 ***	−15.830 ***	−0.568	−0.764	0.871	−1.750 ***
	(0.000)	(1.018)	(1.440)	(0.677)	(0.504)	(1.295)	(0.522)
+7 ha	−27.423 ***	−25.118 ***	−17.343 ***	0.895	−1.235 **	3.827 ***	−1.374 ***
	(2.108)	(2.045)	(1.540)	(0.581)	(0.481)	(1.328)	(0.479)
Farmers’ mode of acquisition of farmland (Ref.: Inheritance)
Rental	16.848 ***	18.208	−19.011	1.667 **	−0.638	2.493 *	−0.877
	(0.847)	(0.000)	(0.000)	(0.813)	(0.709)	(1.369)	(0.711)
Loan	2.567 *	1.783	1.438	0.191	−0.663	0.090	−0.995 **
	(1.364)	(1.241)	(1.200)	(0.543)	(0.423)	(1.258)	(0.442)
Purchase	0.388	1.523	0.829	−0.516	−0.402	3.147 *	−1.052
	(2.093)	(1.704)	(1.473)	(1.036)	(0.615)	(1.632)	(0.691)
Other	22.235 ***	22.857	−15.759	11.411 ***	10.576	−7.524	27.429
	(1.384)	(0.000)	(0.000)	(1.317)	(0.000)	(0.000)	(0.000)
Strategy protects cultivated land (Ref.: Land registration)
Get a title deed	−0.144	−0.190	−0.135	−0.271	−0.333	−1.582 *	−0.482
	(0.896)	(0.820)	(0.686)	(0.388)	(0.278)	(0.808)	(0.321)
Secure by fencing	−1.898 **	−1.384 **	−0.687	0.244	−0.034	−1.997 **	−0.423
	(0.756)	(0.678)	(0.590)	(0.389)	(0.286)	(0.898)	(0.313)
Orchard farming	4.757 *	4.973 *	4.110 *	−0.512	0.702	−0.742	−0.843
	(2.821)	(2.738)	(2.420)	(1.061)	(0.804)	(1.270)	(0.707)
No strategy	−0.176	0.920	0.868	0.933	1.019 **	0.328	−0.354
	(1.419)	(1.345)	(1.273)	(0.607)	(0.462)	(0.940)	(0.413)
Perceptions of farmers about the quality of cultivated land (Ref.: Very high)
High	0.848	0.935	0.961	−0.057	0.286	−0.744	−0.120
	(0.905)	(0.858)	(0.709)	(0.368)	(0.271)	(0.652)	(0.302)
Moderate	0.788	0.872	1.432 *	−0.796 *	0.528 *	−2.241 *	0.291
	(0.968)	(0.887)	(0.799)	(0.464)	(0.309)	(1.227)	(0.345)
Poor	16.713 ***	13.469	−21.444	11.231 ***	10.832	−24.610	−1.760 **
	(4.418)	(0.000)	(0.000)	(1.058)	(0.000)	(0.000)	(0.874)
Quality’s seed (Ref.: Certified)
Not Certified	0.255	−0.654	1.022	0.477	−0.260	−0.151	−0.128
	(1.181)	(0.979)	(0.818)	(0.470)	(0.406)	(0.878)	(0.430)
Do not know	9.444	9.826 ***	9.566 ***	1.003	0.065	2.913 **	0.821
	(0.000)	(0.673)	(1.247)	(0.713)	(0.574)	(1.484)	(0.676)
Seed’s origin (Ref.: Purchase)
Own reserve	−0.251	−2.026 *	−0.206	−0.133	−0.012	−2.207 **	−0.234
	(1.314)	(1.139)	(1.007)	(0.517)	(0.445)	(1.008)	(0.477)
Government subsidy	−8.012 ***	−6.025 ***	−4.660 ***	−25.126	−1.326 **	−32.598	−1.149 **
	(2.424)	(1.968)	(1.362)	(0.000)	(0.523)	(0.000)	(0.561)
Whether farmers practice crop rotation or not (Ref.: No)
Yes	−1.753 **	−1.316 *	−0.404	−0.086	0.150	0.674	−0.460
	(0.825)	(0.768)	(0.710)	(0.344)	(0.271)	(0.643)	(0.292)
Whether farmers practice traditional ploughing or not (Ref.: No)
Yes	−2.693	−1.378	10.993	−0.360	−0.491	11.933 ***	−0.841 *
	(2.147)	(2.135)	(0.000)	(0.539)	(0.377)	(1.921)	(0.432)
If farmers are aware of the law regarding protected farmland (Ref.: No)
Yes	−3.982 ***	−4.334 ***	−3.319 ***	−0.388	−0.452	0.795	−0.383
	(1.221)	(1.192)	(1.145)	(0.367)	(0.279)	(0.666)	(0.313)
If farmers have received help to fight against farmland degradation (Ref.: No)
Yes	0.136	−0.471	0.591	0.058	−0.203	1.256	−0.403
	(0.994)	(0.833)	(0.728)	(0.508)	(0.394)	(0.789)	(0.413)
Constant	31.520 ***	27.816 ***	−4.023	−0.619	2.737 ***	−13.734	3.961 ***
	(3.555)	(3.398)	(3.079)	(0.908)	(0.689)	(0.000)	(0.790)
Observations	583	583	583	583	583	583	583

Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

).

4. Discussion

4.1. Agricultural Productivity Enhancement and Its Challenges

Agricultural productivity, driven by strong grain production capabilities, is essential for maintaining food security and economic stability. The results reveal several challenges to enhancing agricultural productivity in rural Senegal. In recent decades, the number of young farmers has declined in many developed nations, including the United States and various European countries [65]. Attitude, as a cognitive trait, may show potential links to factors like personal awareness, educational attainment, and participation in extension training programs [52]. Senegal is an exception in this reality, because age and educational factors show that younger, less-educated farmers face more difficulties in achieving high production levels despite possessing practical knowledge. In contrast, education plays a crucial role in land protection practices [1]. In theory, human capital suggests that education enhances productivity by equipping farmers with skills to manage land, adopt technologies, and improve efficiency, addressing gaps in practical knowledge [66,67]. Furthermore, social and structural inequalities limit less-educated farmers’ access to resources [68,69], hindering productivity, while education enhances mobility and access to opportunities. So, according to the role of youth in agriculture, this situation suggests that targeted training, modern farming practices, and mentorship can help younger, less-educated farmers improve productivity. The transfer of agricultural land across generations leads to land fragmentation, posing a significant challenge to agricultural development [3,70].

Additionally, older farmers with extensive experience fail to capitalize on technology and effective input use, further exacerbating low productivity. The percentage of farm household members with agricultural education varied from 5.34% in 2017 to 0.67% in 2018 [71]. From then on, challenges in Senegal’s agriculture have included declining education, low technology adoption, and limited access to effective farming inputs. Digital technologies help small-scale farmers overcome certain limitations and engage more effectively in agricultural value chains [72]. Government subsidies negatively affect agricultural output, suggesting that reliance on external financial support hampers self-sufficiency. The paradox of larger land sizes correlating with lower productivity points to issues in land management. It is in this context that efficient land use has become the core concept in socio-economic development [17]. Moreover, gender disparities limit women’s ability to fully contribute to agricultural growth due to restricted access to resources and decision-making power. In a nutshell, challenges in Senegal’s agriculture include declining agricultural education, low technology adoption, limited input access, ineffective government subsidies, poor land management, and gender disparities hindering productivity and growth.

4.2. Agricultural Productivity Enhancement and Its Opportunities

The growth of small-scale agriculture supports sustainable food security and impacts three key dimensions of the SDGs, specifically eradicating poverty and achieving zero hunger [72]. Despite the challenges, from the results, we can point out numerous opportunities for boosting agricultural productivity in rural Senegal. Agricultural policies played a significant role in driving changes in and transforming farmers’ planting decisions [73]. Political and economic reforms are essential for agriculture, as access to land remains a significant challenge beyond financial resources, with the land tenure status also posing an obstacle to securing credit [3]. Younger farmers, although facing challenges, have the potential to benefit from targeted agricultural training and technological advancements. Moreover, the higher productivity of farmers without formal education suggests that practical knowledge could be nurtured through tailored programs that emphasize hands-on skills. Expanding the use of certified seeds and promoting crop rotation can also enhance production.

Additionally, ecological compensation for cultivated land is also an important approach to balance the protection and use of cultivated land ecosystems [74,75]. So, improving land management, particularly in terms of smaller, more efficiently managed plots, may lead to higher yields. The potential impact of crop rotation is an important environmental and health concern and can improve agricultural production [76]. Conventional and scientific cropping patterns are important in realizing the sustainable utilization of soil and promoting the high-quality development of agriculture [77]. Gender-sensitive policies that address women’s access to resources and land would also foster a more inclusive and productive agricultural sector. In addition to this, the value of ecosystem services is crucial in reconciling the demands of food production and ecological conservation [78]. From agricultural frameworks, digital inclusive finance can significantly alleviate financing constraints [73,79]. In a nutshell, opportunities to enhance agricultural productivity in Senegal include targeted training for younger farmers, practical knowledge programs, crop rotation, improved land management, gender-sensitive policies, and digital inclusive finance for financing constraints.

4.3. Policy Implications

The findings call for several policy interventions aimed at addressing both systemic barriers and empowering farmers to improve agricultural production. First, policymakers should focus on providing better access to agricultural training and technology, particularly for farmers with limited formal education and older farmers who may lack technological adoption. This process includes the implementation of localized training programs, mobile technology platforms, and mentorship networks to enhance agricultural skills for Senegal’s underserved farmers. Second, redirecting government subsidies toward more effective, need-based support mechanisms would likely improve farm productivity. Third, the policies that promote land tenure security, including gender-responsive land reforms, are essential for fostering equitable growth. Lastly, prioritizing the use of certified seeds (41.5% do not use them) and supporting sustainable practices like crop rotation (45% do not practice it) are essential for long-term productivity improvements in rural Senegal. The practical policies recommend targeted support for young farmers, including mentorship, microfinance, and crop insurance schemes. It advocates for reforming subsidies with vouchers for sustainable inputs and linking them to good practices. Policies should focus on gender inclusivity, practical training, cooperative farming models, and improved market access for smallholders. In sum, as noted in the previous studies, there is a need to work on the infrastructure front to rebalance the country’s demographic and socio-economic structure [80,81].

In addition, while the findings highlight the need for targeted interventions in agricultural training, subsidies, and land tenure security, the actual impact of these interventions might differ from what is projected based on self-reported data. Accordingly, policymakers should consider additional data sources, such as field observations, to validate the findings. In essence, the findings may still be subject to under-reporting and social desirability bias. These biases could affect the accuracy of reported agricultural productivity levels, income status, and sown area dynamics. Consequently, the policy recommendations derived from this study can be considered in conjunction with additional data sources to validate the findings.

4.4. Potential Limits and Future Research of This Study

Although the significance of our results is significant, this study may have several potential limitations. It primarily relies on self-reported data, which may be subject to biases or inaccuracies. The cross-sectional nature of the analysis limits causal inferences between variables. Moreover, Senegal has diverse ecological zones, and farmers’ characteristics vary across these regions [82]. Therefore, regional differences within Senegal were not fully explored, which may affect the generalizability of the results. Additionally, long-term data are needed to assess the full impact of factors like education on agricultural production. The study also did not consider environmental factors, such as climate change, that may significantly influence agricultural productivity. Lastly, the focus on certain demographic groups may overlook the experiences of other rural farmers.

In addition to these issues, our results do not fully integrate the natural aspects, which can hide agricultural production. Agriculture consumes 70–72% of global freshwater withdrawals, making it the largest user of freshwater resources [83]. Drought, a lack of rainfall, and decreasing soil fertility from climate extremes were identified as the two most common risks by 96% of the smallholder households surveyed [84]. Soil salinity poses a major challenge to global agricultural production, while low soil fertility and profitability impact household sustainability differently [36,85]. Therefore, further research could integrate, at the same time and for a long period, the socio-economic and natural factors to decipher the full issues of agricultural production in Senegal.

5. Conclusions

This research examines the drivers behind the imbalance between operating and embedded production, income, and cultivated areas in Senegal’s agriculture through a gendered lens. Using a multinomial probit model, the study identifies significant heterogeneity, highlighting complex relationships among demographics, resource endowment, farming systems, and institutional support at the household level.

Key findings include that respondents aged 31–40 show consistently negative coefficients across all production categories, indicating a significantly lower agricultural output compared to younger farmers, while older farmers (over 60) report stable income but lower productivity, likely due to declining health, a lack of labor support, and risk aversion. Education negatively impacts agricultural performance, as educated farmers perform worse than uneducated ones, suggesting that practical experience is more critical than formal education. Experience and training also show conflicting results, with farmers having 10–20 years of experience exhibiting lower productivity and training programs yielding limited benefits.

Government subsidies, like subsidized fertilizers and seeds, fail to boost production or sown areas, while land acquisition methods such as rental or purchase improve income. Gender, though not statistically significant in the models, plays a crucial role in agricultural outcomes, with unequal gender relations limiting women farmers’ potential. The study calls for gender-sensitive policies and tailored strategies to foster sustainable and inclusive agricultural development. This study’s limitations include reliance on self-reported data, the cross-sectional analysis, regional differences, and the exclusion of environmental factors. Future research should integrate socio-economic and natural factors over extended periods.