The reported analysis assessed community knowledge through yes/no items and perceptions and practices through five-point Likert-scale items. Composite and mean scores were categorized using predefined intervals ranging from very low to very high. Most respondents received composite awareness and perception scores in the “good” category, defined as 50–75% of the maximum attainable score. The study used correlation and multiple linear regression to examine relationships between these measures and adoption of soil and water conservation practices.

The abstract reports strong positive correlations among the community measures. Knowledge was correlated with perception at r = 0.64, with practice at r = 0.83 and with perception and practice at r = 0.69; all reported p-values were below 0.05. These correlations and regression coefficients indicate statistical associations within the study area rather than demonstrating that any factor caused adoption. The authors identified institutional support, training, information access, extension services and land-tenure security as areas relevant to sustainable adoption, based on the study’s measured associations.

The findings are based on research conducted in one watershed in the highlands of northwestern Ethiopia, so their direct applicability beyond that setting is uncertain. The available abstract does not report the number of survey respondents or key informants, sampling procedures, confidence intervals, model diagnostics or detailed measures of adoption. It also does not establish whether the reported relationships persist over time. Accordingly, the results identify factors statistically associated with reported conservation practice in the Jema Watershed but do not quantify the effect of an intervention, establish causal mechanisms or demonstrate downstream environmental outcomes.

Subject of Research: Household determinants associated with soil and water conservation adoption in Ethiopia’s Jema Watershed

Article Title: Determinants of communities’ adoption and practice of soil and water conservation measures in the Jema Watershed, in the highlands of northwestern Ethiopia

Article References: Fentie, A. Y., & Assefa, Y. (2026). Determinants of communities’ adoption and practice of soil and water conservation measures in the Jema Watershed, in the highlands of northwestern Ethiopia. Discover Sustainability. https://doi.org/10.1007/s43621-026-04965-1

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04965-1

Keywords: soil and water conservation, Ethiopia, Jema Watershed, adoption, household survey, key-informant interviews, multiple linear regression, extension services, land tenure security, rural livelihoods, Determinants, communities

The research, conducted in 2023, focused on Dhurkot rural municipality, a mandarin-growing zone lying between latitudes 28°04’08.76″N and longitudes 83°18’24.84″E at an average altitude of 1,000 to 1,500 meters. The team surveyed 80 households drawn by simple random sampling from a registered sampling frame of 385 mandarin farmers listed in the database of the Prime Minister Agriculture Modernization Project, the largest undertaking of Nepal’s Ministry of Agriculture and Livestock Development. Sample size was calculated using Cochran’s formula at a 95 percent confidence level with a 5 percent margin of error. Data collection combined structured interviews with key informant interviews and focus group discussions, and the resulting dataset was analyzed descriptively using MS Excel and SPSS version 23.0. To prioritize the problems farmers face, the researchers computed an index of importance from farmers’ ranked perceptions of each constraint.

The demographic picture that emerged is one of an aging, experienced and predominantly male farming population. Men made up 76.2 percent of respondents, and the largest age cohort, 41.3 percent, fell between 46 and 60 years old. Only 7.5 percent of those surveyed were between 18 and 30, a striking signal that young people are largely absent from the orchards. Farming experience ran deep: 62.5 percent of respondents had worked their mandarin groves for more than 15 years, while just 1.25 percent had entered the enterprise within the past five years. Educational attainment was modest, with 71.3 percent reporting only primary schooling and 2.5 percent education beyond class 10. Agriculture was the primary income source for 78.8 percent of households, and the average land holding of 0.72 hectares exceeded the national average of 0.55 hectares, though 83.75 percent of farms covered more than 0.25 hectares.

Understanding the soils themselves proved central to the study. Farmers reported three dominant soil types across the orchards: luvisols, reported by 61.25 percent of respondents; cambisols, reported by 36.25 percent; and sandy gravel soils, reported by 2.5 percent. All three are highly prone to erosion under the intense monsoon conditions of the mid-hills. Despite this, soil testing was strikingly rare. Some 72.5 percent of farmers had never conducted a soil test in their orchards, only 18.8 percent tested occasionally, and a mere 1.3 percent determined fertilizer doses based on soil tests and formal analysis. Just 15 percent had measured soil pH and only 2.5 percent had performed nutrient analysis. Instead, 21.3 percent of farmers relied on personal experience to decide fertilizer rates and 20 percent followed the recommendations of agro-input dealers, practices that risk both under- and over-application of nutrients.

Nutrient management in the orchards leaned heavily toward organic inputs. A majority of farmers, 58.75 percent, applied only organic manure, while 40 percent used an integrated nutrient management approach combining organic and inorganic sources; just 1.25 percent relied exclusively on chemical fertilizers. Farmyard manure doses rose steadily with tree age, from an average of about 4 kilograms per plant in the first year to nearly 47 kilograms per plant by the sixth year and beyond. Among chemical fertilizers, urea was the universal nitrogen source, while diammonium phosphate, single super phosphate and muriate of potash supplied phosphorus and potassium in escalating annual doses. Notably, farmers reported that organic fertilizers improved yield, fruit quality and shelf life, an observation consistent with soil nutrient trials on other crops. Micronutrients were also widely used, with 91.3 percent of respondents applying them, foliar zinc being the most common, reflecting the well-documented importance of micronutrient nutrition in citrus productivity.

The headline finding of the study is the remarkably high adoption of soil conservation practices. Fully 96.2 percent of respondents had adopted at least one conservation measure in their orchards. Terracing and cover cropping led the list at 95 percent, followed by sod culture and liming at 40 percent each, minimum tillage at 36.2 percent, contour farming at 31.3 percent, and integrated plant nutrient system adoption at 31.2 percent. Traditional techniques persisted alongside modern ones: stone bunds, mulching, terrace ditches, bamboo fencing and the planting of local trees and grasses remained common features of the hillside orchards. Mulching was overwhelmingly organic, with 85 percent of practitioners using maize straw, grasses or forest litter, applied most often yearly and timed to the dry months from December to April when soil moisture is low. Only 1.3 percent used plastic mulch, and hand-hoeing dominated weed control at 98.8 percent, with synthetic herbicides nearly absent.

Farmers themselves reported tangible payoffs from these practices. Some 55.84 percent said conservation measures had visibly reduced soil erosion on their land, 41.56 percent reported increased yields, and smaller shares noted reduced moisture stress and lower labor requirements. A strong majority, 81 percent, agreed that soil conservation practices delivered concrete economic benefits through reduced production costs and higher farm income while curbing erosion, landslides and localized fertility decline. Awareness was similarly high: 95 percent of respondents recognized soil degradation and its consequences for mandarin production, identifying heavy monsoon rainfall as the principal driver, and noting outcomes such as short-term fertility losses and higher yields of poorer fruit quality. Yet only 31.2 percent were familiar with the negative effects of chemical fertilizers on soil health, pointing to a knowledge gap in nutrient stewardship.

Adoption, however, was far from frictionless. A striking 78 percent of respondents described soil conservation practices as time-consuming and cumbersome, and only 21.3 percent, typically the more educated farmers, expressed willingness to adopt new conservation technologies. When problems were ranked, the lack of proper irrigation and quality inputs such as planting materials, fertilizers, pesticides and tools emerged as the most serious constraint, followed by diseases and pests, poor extension services regarding soil conservation, inadequate marketing facilities and annual fruit drop. Water scarcity plagued 88.2 percent of respondents, who coped through traditional drip irrigation, rainwater harvesting and improved drainage. The study also identified weak links in the wider value chain: mandarin marketing in the area was described as unorganized and prone to seasonal gluts, lacking systematic post-harvest technology, transportation and storage infrastructure.

The analysis made clear that awareness and adoption of conservation practices were shaped by a cluster of factors, including farming expertise, education, training participation, landholding size and contact with extension agents. These findings echo earlier research on technology adoption in smallholder systems, which consistently finds that extension contact and capacity building are decisive in shifting farmer perceptions. The authors conclude that strengthening the frequency and quality of extension services, delivering knowledge through radio, television, newspapers and social media down to the grassroots level, and formulating strategic plans to address the constraints farmers face are essential steps. They also recommend blending indigenous practices such as mulching, stone bunds and contour farming with modern approaches including scientific orchard layout, sod culture, sloping agricultural land technology and integrated plant nutrient management.

The stakes extend well beyond Gulmi. Citrus contributes about 21.61 percent of Nepal’s total fruit production, and mandarins alone accounted for 185,346 metric tons from 19,481 hectares nationally in the 2021/22 fiscal year, with Gulmi producing 7,164 metric tons from 597 hectares. Yet the Nepal Agriculture Research Council has warned of a broad citrus decline affecting orchards nationwide, driven in part by neglected soil and poor orchard management that stunt tree growth and invite pests and disease. With agriculture employing 57.1 percent of Nepal’s population and contributing nearly 24 percent of GDP, the fate of hillside mandarin orchards is a matter of national economic weight. This study suggests that the knowledge and the practices needed to sustain them already exist among farmers; the challenge now is to close the extension gap so that conservation becomes the rule rather than the exception across Nepal’s citrus hills.

Subject of Research: Adoption of soil conservation practices among mandarin farmers in Gulmi district, Nepal

Article Title: Adoption of soil conservation practices by mandarin ( Citrus reticulata B.) farmers in Gulmi, Nepal

Article References: Khang, K., Yadav, M., & Jha, M. K. (2024). Adoption of soil conservation practices by mandarin (Citrus reticulata B.) farmers in Gulmi, Nepal. Innovations in Agriculture, 7, 1-9. https://doi.org/10.3897/ia.2024.134984

Image Credits: AI Generated

DOI: 10.3897/ia.2024.134984

Keywords: soil conservation, mandarin orange, Nepal, citrus farming, soil erosion, Gulmi district, extension services, orchard management, terracing, mulching, integrated nutrient management, smallholder farmers

The research team, led by Joshua Muloi of the Center for Development Research at the University of Bonn, conceptualized regenerative agriculture adoption not as a single decision but as a continuum of engagement. Working across two contrasting Kenyan counties—semi-arid Makueni in the southeast and the humid, commercially intensive Kirinyaga on the slopes of Mount Kenya—the researchers combined an exploratory qualitative phase with a large household survey. The qualitative work involved 33 individual farmer interviews, 14 focus group discussions, 9 expert interviews, and a stakeholder workshop with 196 participants, conducted in Swahili, Kamba, and Kikuyu languages between September and December 2024. These insights then informed the design of a survey administered to 949 farmers between August and September 2025.

Regenerative agriculture is organized around five core principles rather than a fixed package of technologies: minimizing soil disturbance, maximizing soil cover, increasing agrobiodiversity, maintaining living roots in the soil, and integrating livestock into cropping systems. Because farmers engage with combinations of interdependent practices rather than a single technology, the researchers argue that conventional binary measures collapse substantively different forms of engagement into one misleading distinction. A farmer experimenting with zai pits on a small plot, a farmer who delayed uptake for years after first hearing about the approach, and a farmer who abandoned the practices altogether may all be lumped together as non-adopters—or, conversely, as adopters—despite occupying fundamentally different positions.

To capture this heterogeneity, the team developed a seven-state typology based on four interconnected dimensions: awareness, uptake timing, continuity of use, and post-adoption behavioral adjustment. The states distinguish farmers who are not aware of regenerative agriculture, aware non-adopters, disadopters who previously practiced but stopped, trailing adopters who experienced delayed uptake, new adopters, plateau adopters who maintain practices without intensifying, and intensifying adopters who expand their engagement over time. The classification draws on social practice theory, which conceptualizes change as the reconfiguration of everyday practices through the interaction of materials, competences, and meanings rather than as a sequence of isolated individual decisions.

The results expose how much the binary lens conceals. A conventional classification would identify 49 percent of surveyed farmers as current implementers and 51 percent as non-implementers. But the seven-state breakdown reveals that within the non-implementer group, 33.7 percent had never heard of regenerative agriculture, 13.8 percent were aware but had not implemented it, and 3.5 percent had previously practiced and discontinued. Among implementers, 11.1 percent were trailing adopters with delayed uptake, 20.7 percent were new adopters, 2 percent had plateaued, and 15.3 percent were intensifying their engagement. The researchers emphasize that these states represent cross-sectional positions consistent with potential adoption pathways rather than directly observed transitions over time.

Qualitative evidence reinforced the quantitative patterns. Farmers described testing practices on small portions of their land before expanding, with one reporting that she started on one-eighth of an acre and subsequently expanded to three-quarters of her property. Another explained that each farmer picked what worked for their own farm from the range of practices they had been trained on. Discontinuation took varied forms: some farmers stopped individual practices that produced limited benefits or did not fit their conditions, while others withdrew from groups implementing the approach because of heavy labor requirements or unmet expectations of financial support. In the coffee-growing context of Kirinyaga, farmers described converting gradually to avoid disrupting existing production, for instance by grafting a limited number of coffee stems at a time rather than changing the entire farm at once.

Using a multinomial logit model with the not-aware group as the base outcome, the researchers examined which socioeconomic and institutional factors were associated with membership in each state. Credit access emerged as the most consistent correlate across engagement states beyond awareness. Farmers with access to credit had a 12-percentage-point higher predicted probability of being new adopters, while their probabilities of being not aware and aware non-adopters were 14.3 and 6.7 percentage points lower, respectively. Qualitative accounts provided a plausible mechanism: farmers described needing financial resources to hire labor, obtain manure, and acquire other inputs required for implementation. In Kirinyaga, some farmers accessed loans or advances through coffee cooperatives and tea factories connected to the Rainforest Alliance’s Mount Kenya Sustainable Landscape and Livelihoods programme.

Other factors showed more selective and sometimes counterintuitive associations. Land tenure security was negatively associated with being an aware non-adopter, disadopter, or trailing adopter relative to not being aware, but positively—though weakly—associated with intensifying adoption. The qualitative findings help explain why formal ownership should not be equated with operational control: participants described households where land was registered to men while women managed the farming, or where members cultivated separate portions. Marriage was positively associated with trailing and intensifying states, but the researchers interpret this as a marker of heterogeneous household decision and support arrangements rather than a single mechanism. Men were less likely than women to be classified as disadopters or new adopters relative to the not-aware category, while other gender comparisons were not statistically significant. Farm income was not significantly associated with any engagement state, and off-farm income was only weakly and negatively associated with intensifying adoption, suggesting that resource endowments alone are insufficient to explain engagement patterns.

The study identifies two critical transition gaps. First, a third of all farmers were unaware of regenerative agriculture, indicating an awareness gap that reflects limited knowledge diffusion among smallholders. Second, aware non-adopters, farmers with delayed uptake, and disadopters highlight a conversion gap: awareness alone does not resolve the practical constraints of labor requirements, limited income, perceived risks, and the need to observe convincing results before committing resources. The predominance of new adopters points to a recent wave of initial uptake, potentially linked to growing visibility of the approach through governmental and non-governmental programmes. Meanwhile, plateau engagement emerges as a distinct and relatively underexplored state in which farmers maintain practices without further intensification, for reasons the researchers hypothesize may include standardized training framings that limit continued experimentation, biophysical and production constraints, deliberate maintenance when benefits of expansion do not outweigh costs, or social identity dynamics that confer recognition without incentivizing further change.

The findings carry direct implications for how regenerative agriculture is promoted and financed. The researchers argue that extension systems and public-private advisory services should shift from one-off dissemination toward long-term accompaniment that supports iterative learning and progression across engagement states. Subsidies, grants, concessional credit, or in-kind support could help reduce the financial constraints that appear to bind most tightly during initial implementation, when farmers face upfront costs and delayed or uncertain returns. Programme monitoring systems could incorporate indicators of awareness, adoption timing, implementation intensity, and discontinuation to identify farmers requiring different forms of support. The seven-state typology, the team suggests, offers a potentially transferable framework for diagnosing where farmers sit along the engagement continuum and for designing state-contingent interventions that address both entry barriers and progression constraints—moving the field beyond the binary categories that have long obscured the differentiated reality of agricultural transitions among smallholders.

Subject of Research: Heterogeneous states of regenerative agriculture adoption among smallholders in Kenya

Article Title: Heterogeneous states of regenerative agriculture adoption among smallholders in Kenya

Article References: Muloi, J., Kamau, H., Betancur-Corredor, B., Kadenge, E., Rwanda, C., Wario, D. K., Stellmacher, T., Borgemeister, C., & Beuchelt, T. D. (2026). Heterogeneous states of regenerative agriculture adoption among smallholders in Kenya. Discover Sustainability, 7(1), Article 1737. https://doi.org/10.1007/s43621-026-05058-9

Image Credits: AI Generated

DOI: 10.1007/s43621-026-05058-9

Keywords: regenerative agriculture, smallholder farmers, Kenya, adoption pathways, credit access, mixed methods, multinomial logit, soil health, extension services, disadoption, agroecology, sustainability transitions

The stakes could hardly be higher. Agriculture anchors Ethiopia’s economy, contributing roughly a third of gross domestic product and employing between 80 and 85 percent of the population. With arable land scarce, raising productivity per hectare is the primary pathway to national food security, and inorganic fertilizers such as urea and NPS are central to that strategy. Previous research has shown what is possible when inputs are used properly: combining recommended fertilizer rates with improved crop varieties can multiply yields two to three times, and fertilizer paired with optimal sowing dates and agronomic practices has boosted grain yields by 122 percent compared with untreated controls. Yet in the northeastern districts of the Amhara Region, adoption and application rates remain stubbornly low, and most earlier studies of fertilizer behavior were conducted in agroecological settings with rainfall patterns, soil fertility, and climatic conditions that differ markedly from these drylands.

To fill that evidence gap, the researchers purposively selected three districts known for low or negligible fertilizer use: Sekota in the Wag-himra zone, Tehuledere in South Wollo, and West Belesa in Central Gondar. The districts span a remarkable range of conditions. Sekota sits at altitudes of 1,500 to 2,200 meters and receives a meager 350 to 650 millimeters of erratic annual rainfall. Tehuledere, by contrast, enjoys roughly 1,030 millimeters of rain a year at elevations reaching 2,900 meters. West Belesa, a lowland sorghum zone, endures average temperatures of 33.5 degrees Celsius. From a population of 1,673 cereal-producing households, the team drew a stratified sample using multistage sampling, ultimately analyzing 304 valid observations after a 2.875 percent non-response rate. Households were split between fertilizer users and non-users living in the same kebeles, allowing direct statistical comparison of the two groups.

The methodological heart of the study is the Double Hurdle model, an econometric framework originally developed by Cragg in 1971. Unlike the Tobit model, which forces adoption and intensity into a single underlying process, or the Heckman selection model, which is poorly suited to corner solutions where many observations are genuine zeros, the Double Hurdle model treats the adoption decision and the intensity of use as two independent stochastic processes. The choice was not arbitrary. A likelihood ratio test statistic of 2,344.287 vastly exceeded the critical chi-square value of approximately 20.337 at the five percent significance level, and the model posted a lower Akaike Information Criterion than the Tobit alternative. An insignificant inverse Mills ratio confirmed the absence of sample selection bias, ruling out the need for a Heckman correction. Variance inflation factors showed no problematic multicollinearity among the explanatory variables.

The descriptive statistics alone tell a compelling story. Sixty-three percent of sampled households used fertilizer while 37 percent did not, but the two groups diverged sharply on institutional and cognitive variables rather than on demographics. A decisive 85.34 percent of users had access to extension services, compared with just 37.17 percent of non-users, a difference significant at the one percent level. Even more dramatic was technical knowledge: 72.77 percent of users understood fertilizer application techniques, while only 11.50 percent of non-users did. Cooperative membership also differed significantly, with 80.10 percent of users belonging to cooperatives against 68.14 percent of non-users. Intriguingly, the continuous variables showed no significant differences between groups. Age, family size, farm experience, livestock holdings, farm size, market distance, and income were statistically indistinguishable between users and non-users, suggesting that the adoption divide is institutional and informational rather than a matter of who farmers are.

The probit stage of the model quantified these effects. Each additional extension contact raised the probability of adoption by 2.1 percent, while farmers with adequate application knowledge were 13 percent more likely to use fertilizer than those without. Favorable perceptions of fertilizer added 3.7 percentage points to adoption probability, confirming that subjective beliefs about yield benefits matter alongside objective constraints. Geography and price worked in the opposite direction: every additional minute of walking time to an input distribution center reduced the likelihood of adoption by 0.02 percent, and higher fertilizer prices cut adoption probability by 2.0 percent. These marginal effects may appear small individually, but in communities where input centers are an hour’s walk away and prices fluctuate with import costs, they compound into the stark adoption disparities observed between villages.

The truncated regression stage, modeling intensity among adopters, produced its own surprises. Knowledge of fertilizer application emerged as the single most powerful driver: households with a firm grasp of application techniques applied 30.18 kilograms more fertilizer per hectare than their less-informed counterparts, an effect significant at the one percent level. Cooperative membership added 11.69 kilograms per hectare, reflecting the power of collective bargaining and shared knowledge in connecting farmers to inputs. Annual income exerted a positive but tiny per-unit effect, its significance lying in what it represents: the liquidity that resource-poor households need to purchase inputs at all. Market distance again proved a barrier, with each additional minute of travel reducing application by 0.189 kilograms per hectare. Fertilizer price negatively affected intensity as well, and prior experience with fertilizer application contributed positively.

Perhaps the most counterintuitive result concerns farm size. Each additional hectare of land was associated with a 9.27 kilogram reduction in fertilizer applied per hectare, an effect significant at the one percent level. The authors interpret this as a strategic trade-off: farmers with larger holdings prioritize total volume, spreading limited resources across a broader landscape, while smallholders treat every plot as a critical asset and apply inputs intensively to maximize yield per square meter. This pattern echoes earlier findings from Ethiopia and elsewhere showing that smaller-scale farmers invest more heavily in per-unit land productivity to compensate for their lack of acreage. For policymakers, it suggests that blanket fertilizer subsidies may be absorbed differently depending on farm scale, and that per-hectare targeting may be more effective than per-household distribution.

The fertilizer intensity index laid bare the magnitude of the underutilization problem. On average, farmers applied just 19.01 kilograms of urea per hectare and 24.32 kilograms of NPS per hectare, against national recommendations of 50 and 100 kilograms respectively. That translates into application gaps of 61.98 percent for urea and 75.68 percent for NPS, meaning that more than three-quarters of the recommended nitrogen and phosphorus nutrition never reaches the region’s sorghum, teff, and cereal fields. Bridging even part of this gap could substantially raise crop yields in a region where food insecurity is chronic. The study’s recommendations follow directly from its two-hurdle structure: strengthen extension services and demonstration trials to shift perceptions and build knowledge, expand decentralized input distribution to cut travel costs, implement market stabilization mechanisms to keep prices predictable, and support cooperatives that demonstrably intensify input use among their members.

What makes this research resonate beyond Ethiopia is its methodological lesson. Because adoption and intensity respond to different levers, interventions that successfully convince farmers to try fertilizer may do little to change how much they apply, and vice versa. A cooperative that supplies affordable urea will not help a farmer who lacks the knowledge to apply it correctly; an extension agent who teaches application techniques cannot help someone who cannot afford the input or reach the depot. In the drylands of Amhara, as the 304 households surveyed here make clear, closing the fertilizer gap requires crossing both hurdles at once.

Subject of Research: Determinants of inorganic fertilizer adoption and application intensity among smallholder farmers in the dryland farming systems of the Amhara Region, Ethiopia

Article Title: An empirical analysis of the determinants of fertilizer adoption and intensity: Evidence from the dryland farming systems of Amhara Region, Ethiopia

Article References: Wubet, A., & Bizuayehu, S. (2026). An empirical analysis of the determinants of fertilizer adoption and intensity: Evidence from the dryland farming systems of Amhara Region, Ethiopia. Innovations in Agriculture, 9, Article e179304. https://doi.org/10.3897/ia.2026.179304

Image Credits: AI Generated

DOI: 10.3897/ia.2026.179304

Keywords: fertilizer adoption, Ethiopia, Amhara Region, smallholder farmers, double hurdle model, dryland agriculture, extension services, food security, agricultural intensification, cooperatives, urea, NPS fertilizer

The research team, led by Debasmita Baruah of the Department of Agricultural Economics at Assam Agricultural University in Jorhat, together with colleagues from institutions including the ICAR National Institute of Agricultural Economics and Policy Research in New Delhi, analysed climatic data spanning 1981 to 2024. Rather than relying on a single summary statistic, the researchers examined four separate parameters that matter directly to agriculture: rainfall, maximum temperature, minimum temperature, and relative humidity. Each was tested for long-term trend using the Mann-Kendall test, a non-parametric method widely used in hydrology and climate science because it makes no assumptions about the underlying distribution of the data and is robust to outliers, and the magnitude of any trend was quantified with Sen’s slope estimator, which computes the median of all pairwise slopes in the series.

The statistical verdict was clear on two fronts. Minimum temperature rose at a rate of 0.034 degrees Celsius per year, a trend that was statistically significant and, compounded over the 43-year record, amounts to a shift of well over a degree in nighttime conditions. Relative humidity increased by 0.18 percentage points per year, also a significant trend. Rainfall, by contrast, showed a positive direction but failed to reach statistical significance, meaning the data cannot rule out that the apparent increase is noise. This asymmetry matters agronomically. Warmer nights accelerate crop respiration, erode the accumulation of dry matter in grains, and raise the vapour pressure deficit stress on plants, while persistently higher humidity favours fungal and bacterial pathogens that attack rice, vegetables, and pulses. A climate that is not dramatically hotter by day can still be substantially more hostile to farming.

Assam’s agriculture is dominated by small and marginal holdings, and the state’s position in the Brahmaputra valley exposes it to floods, erosion, and increasingly erratic monsoon behaviour. To understand how farmers are responding, the team turned to primary data collected from 300 farmers using a multistage stratified random sampling technique, a design that first divides the population into meaningful strata, in this case across three agro-climatic zones, and then samples randomly within them at successive stages. The survey focused on the Central Brahmaputra Valley Zone, and the authors acknowledge the farmers of that zone for their cooperation. Ethical approval for the survey was obtained from the Institutional Ethics Committee of Assam Agricultural University, and informed consent was taken from all participants.

From the survey responses, the researchers constructed an adaptation index, a composite score that measures the extent to which each farmer has adopted recognised climate-change adaptation practices. The results were striking in their hierarchy. Crop diversification emerged as the most widely adopted strategy, with an index score of 0.99, meaning it was essentially universal among the sampled farmers. Spreading production across multiple crops is a classic risk-spreading manoeuvre: if floods or pests wipe out one crop, others may survive, smoothing household income and food supply in the face of climatic shocks. Off-season vegetable cultivation followed with a score of 0.87, a strategy that both exploits market windows when prices are favourable and shifts production away from the periods of highest climatic risk. Crop-livestock integration ranked third at 0.71, providing farmers with a buffer of animal products and manure that decouples part of household income from the fate of the fields.

But knowing what farmers do is only half the question. The more policy-relevant puzzle is who adapts and why. To answer it, the team employed binary logistic regression, a statistical model suited to a binary outcome, in this case whether or not a farmer has adapted to climate change. The model estimates the log-odds of adaptation as a function of socio-economic explanatory variables, allowing each factor’s effect to be isolated while holding the others constant. The results pointed unambiguously to two drivers. Farm size was highly significant, with a p-value below 0.001, and extension contact, the number of times a farmer interacts with agricultural advisory services per year, was also significant at p equal to 0.007.

The effect sizes translate into concrete, actionable numbers. A one-hectare increase in farm size was associated with an approximately 16 percentage-point increase in the probability that a farmer adapts. One additional extension contact per year raised the predicted probability of adaptation by roughly 0.97 percentage points, all else being equal. These figures carry a double message. First, larger landholders have both the resources to invest in new practices and the cushion to absorb the risk of trying them, so adaptation capacity is stratified by wealth in exactly the way development economists would predict. Second, and more optimistically, extension contact matters independently of land size, which means that the flow of information from agricultural scientists and field officers to farmers is a genuine lever that policy can pull, and one that does not require redistributing land.

There is a sobering arithmetic hidden in that 0.97 percentage-point figure. If a single extra advisory contact per year buys less than one percentage point of adaptation probability, then moving the needle for a large share of Assam’s farming population will require a sustained, multiplicative expansion of extension services, training programmes, and climate-smart technology delivery, not a one-off campaign. The authors argue that improving farmers’ access to extension services, training, and climate-smart technologies is the key local-level priority. At the regional level, they note, the findings can help policymakers in areas with similar agro-climatic conditions design strategies to strengthen agricultural resilience and food security, a point of considerable relevance across the eastern Himalayan foothills and the densely farmed river deltas of South Asia, where millions of smallholders face comparable combinations of warming nights, humid air, and erratic monsoons.

What makes the study methodologically interesting is the way it stitches together two kinds of evidence that rarely meet in a single paper. On one side sits a 43-year instrumental record interrogated with trend tests that are standard precisely because they are conservative; on the other, a structured household survey converted into a quantified adaptation index and pushed through a regression model with interpretable marginal effects. The connection between the two is the crux. The climate data establish that the hazard is real and directional, while the survey data establish that the response is uneven and socially patterned. A significant rise in minimum temperature and humidity is not, by itself, a crisis; it becomes one when the farmers least equipped to respond, those with the smallest plots and the thinnest connections to advisory networks, are the ones most exposed.

The broader lesson extends well beyond Assam. Around the world, adaptation to climate change is often discussed as if it were a purely technical problem, a matter of breeding hardier varieties or forecasting floods better. This study is a reminder that adaptation is also a distributional problem. The practices that buffer farmers against climatic risk, diversifying crops, shifting planting calendars, integrating livestock, are already known and already adopted by many. What separates adapters from non-adapters is not knowledge of the practices but the land and the information channels that make adoption feasible. As nighttime temperatures continue their statistically significant climb and humidity keeps rising across the Brahmaputra valley, the study suggests that the cheapest and fastest intervention available to governments may not be a new technology at all, but a more frequent knock on the farmer’s door from someone carrying useful, locally relevant advice. In a state where agriculture remains the backbone of rural livelihoods, that knock could determine who weathers the coming decades and who does not.

Subject of Research: Climate trends and farmer adaptation strategies across agro-climatic zones of Assam, India

Article Title: Climate change and agricultural resilience in Assam: unravelling climate trends, farmer adaptation, and socioeconomic drivers across agro-climatic zones

Article References: Baruah, D., Kishore, P., Rede, G. D., Barman, U., Saikia, H., Majumder, S. H., & Deka, N. (2026). Climate change and agricultural resilience in Assam: unravelling climate trends, farmer adaptation, and socioeconomic drivers across agro-climatic zones. Discover Sustainability. https://doi.org/10.1007/s43621-026-05005-8

Image Credits: AI Generated

DOI: 10.1007/s43621-026-05005-8

Keywords: climate change, agricultural adaptation, Assam, agro-climatic zones, Mann-Kendall test, Sen's slope, crop diversification, extension services, smallholder farmers, logistic regression, agricultural resilience, food security

The research team, led by Stephen Prah and including John N. Ng’ombe, Bright Owusu Asante, and Jonathan Masasi, framed their analysis around a deceptively tricky statistical problem. Farmers who adopt climate-smart technologies are not a random sample of the farming population. They tend to be better educated, better connected to extension agents, and more likely to have access to credit. Any naive comparison between adopters and non-adopters therefore risks conflating the effects of the technologies themselves with the pre-existing advantages of the people who choose them. To untangle this, the authors deployed a dose-response model with Heckman selection correction, a two-stage econometric approach that explicitly models both the decision to adopt and the intensity of adoption, while correcting for the selection bias that would otherwise distort the estimates.

The dose-response framework is what makes this study unusual. Most adoption studies in agricultural economics treat technology use as a binary variable: a farmer either adopts or does not. But in the field, adoption is a continuum. One household might plant a small portion of its land with drought-tolerant maize while continuing business as usual elsewhere; a neighbor might restructure the entire farm around a full suite of climate-smart practices. By measuring adoption intensity, the share of recommended technologies and practices a household actually implements, the researchers could trace out how welfare outcomes change across the entire spectrum of engagement, from minimal uptake to comprehensive adoption.

The headline finding is a U-shaped relationship between adoption intensity and maize yield. At low levels of adoption, yields actually decline relative to non-adopter baselines, a pattern the authors attribute to the transition costs and learning curves that come with new practices. Partial adoption can mean partial disruption: new seeds planted without the complementary fertilizer schedules, or mulching introduced without adjusted tillage, may initially perform worse than the familiar system they replace. But as adoption intensity climbs past a threshold, the curve turns upward, and heavily adopting households pull decisively ahead. On average across the sample, higher adoption intensity was associated with a 21 percent increase in maize yield compared with what farmers would have achieved without the technologies.

Productivity, however, was only half of the story. The researchers also examined household asset accumulation, a slower-moving but arguably more meaningful indicator of long-term welfare. Assets, livestock, equipment, housing quality, and similar holdings, capture whether productivity gains translate into durable improvements in living standards rather than being consumed away season by season. Here again the relationship was nonlinear and positive: greater adoption intensity corresponded to an average 19.5 percent increase in household assets. The parallel results suggest that climate-smart farming is not merely boosting one season’s harvest but is reshaping the economic trajectory of adopting households over time.

Perhaps the most policy-relevant insight concerns heterogeneity. The effects of adoption were not uniform across the sample. Households differed in how much they benefited depending on their resource endowments, education levels, and exposure to climate stress. This matters because development programs often roll out technology packages with the implicit assumption that benefits will diffuse evenly. The Ghanaian evidence suggests otherwise: the same technology bundle that transforms one farm’s fortunes may deliver only marginal gains to another, and the distributional consequences of promotion campaigns deserve as much attention as their average effects.

The study also identified the factors that push farmers toward adoption in the first place. Education emerged as a consistent driver of both the likelihood and the intensity of adoption, reinforcing a well-established pattern in which human capital determines the capacity to absorb and implement complex agronomic information. Access to credit mattered for obvious reasons: many climate-smart inputs, from certified drought-tolerant seed to soil amendments, require upfront cash that cash-constrained households cannot easily mobilize. Contact with agricultural extension services was another strong predictor, since extension agents remain the primary conduit through which technical knowledge reaches rural communities. Finally, farmers who perceived drought as a significant and growing threat were more likely to invest in adaptive technologies, suggesting that climate risk awareness is itself a lever for change.

These determinants point toward a practical agenda. If adoption intensity is what drives the welfare gains, and if the drivers of intensity are education, credit, extension, and risk perception, then policy should aim squarely at deepening adoption rather than simply counting adopters. Programs that celebrate the number of farmers reached with a new seed variety may be missing the point: a farmer who plants 10 percent of her land with drought-tolerant maize may see no benefit at all, while one who reorganizes her whole operation around climate-smart principles reaps gains on the order of a fifth of her yield and assets. The difference between shallow and deep adoption is, on this evidence, the difference between wasted subsidy and transformative investment.

For Ghana, where maize is a staple crop and rainfed agriculture dominates, the stakes are high. Climate projections for West Africa point to rising temperatures, more erratic rainfall, and increased frequency of drought stress, all of which threaten the productivity of smallholder systems that already operate close to the margin. Climate-smart agriculture has been promoted by international initiatives as a triple-win strategy, raising productivity, enhancing resilience, and reducing emissions where possible, but the evidence base for its welfare effects has remained thin, particularly regarding how benefits scale with the depth of adoption. This study helps fill that gap with rigorous causal identification and a design that respects the reality of partial, incremental, and bundled technology use on real farms.

The broader lesson may resonate well beyond Ghana’s maize belt. As governments and donors channel billions into climate adaptation for agriculture, the question of dosage, how much adoption is enough, is becoming central to program design everywhere from the Sahel to South Asia. The Ghanaian evidence offers a clear if demanding answer: partial measures may disappoint, and the transition period may even hurt, but sustained, intensive adoption of climate-smart technologies pays off in both harvests and household wealth. Getting farmers to that threshold will require the unglamorous work of building credit systems, strengthening extension networks, and convincing rural households that a changing climate makes the investment worthwhile. The science of the technologies, in other words, is only half the equation; the economics of getting farmers fully on board is the other half, and it is where the battle for climate-resilient agriculture will be won or lost.

Subject of Research: Effects of climate-smart agricultural technology adoption intensity on maize yield and household welfare in Ghana

Article Title: How much climate-smart agriculture is enough? A dose-response analysis of technology adoption effects on farmer welfare in Ghana

Article References: Prah, S., Ng’ombe, J. N., Asante, B. O., & Masasi, J. (2026). How much climate-smart agriculture is enough? A dose-response analysis of technology adoption effects on farmer welfare in Ghana. PLOS Climate, 5(10), e0000886. https://doi.org/10.1371/journal.pclm.0000886

Image Credits: AI Generated

DOI: 10.1371/journal.pclm.0000886

Keywords: climate-smart agriculture, Ghana, maize, adoption intensity, dose-response analysis, farmer welfare, crop yield, household assets, extension services, credit access, drought resilience, agricultural economics

The research team, led by Fenet Belay of Jimma University together with colleagues at Bayero University Kano and Jimma University, drew on two waves of the Ethiopia Socioeconomic Panel Survey, collected in 2018/19 and 2020/21 under the World Bank’s Living Standards Measurement Study–Integrated Surveys on Agriculture program. After merging the waves and cleaning the data, the final analytical sample comprised 1,400 agricultural households tracked over time. Rather than asking whether farmers used sustainable practices in a single year, the researchers classified each household into distinct trajectories: persistent adopters who used a practice in both waves, new adopters who began between the waves, and non-adopters who used it in neither. A fourth category, dis-adopters, proved too small for reliable statistical analysis, containing only about a dozen households.

The study focused on three practices with sharply different technical profiles. Crop rotation is primarily knowledge-intensive, demanding planning and agronomic management but little cash. Chemical fertilizer use is capital-intensive, requiring a lump-sum cash outlay at the start of each planting season along with functioning input markets. Soil and water conservation, which includes structures such as terraces and bunds, demands heavy labor investment and delivers its benefits gradually through improved soil fertility and moisture retention. This deliberate contrast allowed the researchers to test whether the forces driving adoption differ according to what a practice actually costs a farming household in money, muscle, or management.

The descriptive results reveal striking heterogeneity. More than half of households, 53.6 percent, were persistent adopters of crop rotation, a figure the authors attribute to the practice’s low financial cost and compatibility with traditional farming systems, though 27.17 percent never adopted it. Chemical fertilizer showed a different pattern: 45 percent of households used it in both waves, 21.7 percent were new adopters, and roughly a third, 33.2 percent, never used it at all, likely reflecting constraints on input cost, availability, and credit. Soil and water conservation presented the most transitional picture, with only 36.68 percent persistent adopters, but a remarkable 31.6 percent new adopters, a surge the authors link to public works programs such as food-for-work and watershed management campaigns.

To explain these trajectories, the team employed a pooled multinomial logit model, a statistical framework grounded in random utility maximization in which each household is assumed to select the adoption pathway offering the greatest perceived benefit. Because the dependent variable summarizes behavior across two periods rather than a single snapshot, the researchers used lagged explanatory variables from the 2018/19 baseline, ensuring that the factors predicting trajectories were measured before the outcomes occurred. Standard errors were clustered at the household level to account for correlation across survey waves. The authors are careful to note that the coefficients represent conditional associations rather than causal effects, an honest caveat in a field where unobserved traits such as farmer innovativeness and risk appetite can never be fully measured.

The econometric results deliver a clear message about money. Access to credit increased the probability of persistent adoption of crop rotation by 22.6 percentage points and of chemical fertilizer by 16.2 percentage points, while cutting the probability of non-adoption by 25.5 and 20.0 percentage points respectively. The most dramatic descriptive finding concerned new crop rotation adopters: 73 percent of them had credit access, compared with only 16 to 17 percent of persistent adopters, suggesting that liquidity is a primary catalyst for initiating rotational farming. This makes economic sense, since fertilizer in particular demands a large upfront payment precisely when cash is scarcest, and farmers who cannot borrow either skip the input or abandon it after a single season.

Extension services told an equally important but more puzzling story. Access to agricultural extension raised the probability of persistent adoption by 19.5 percentage points for crop rotation, 24.8 for fertilizer, and 16.0 for soil and water conservation. Yet in the raw data, non-adopters reported the highest rates of extension contact, reaching 80 percent for crop rotation and 75 percent for fertilizer. The authors suggest two possible explanations: extension agents may deliberately target non-users, or the content of existing advisory services may simply be failing to change behavior. Either way, the finding points to a potential misalignment between how extension is delivered and what farmers actually need to sustain new practices over multiple seasons.

Household resources shaped trajectories in ways that varied by practice. Livestock ownership, measured in tropical livestock units, was consistently higher among persistent adopters, averaging 4.09 units for crop rotation and 4.02 for fertilizer use, consistent with integrated crop-livestock systems easing input constraints. Labor availability emerged as decisive for soil and water conservation, where persistent adopters reported an average of 4.26 adult-equivalent workers, reflecting the heavy demands of building and maintaining terraces. Intriguingly, age worked in opposite directions: older farmers tended to persist with rotation and fertilizer, while younger farmers, averaging about 42 years versus 46 for non-adopters, were more likely to sustain conservation investments, plausibly because they have longer horizons over which to reap the gradual benefits. Wealth itself showed a non-linear pattern, with the poorest households persisting in conservation practices, likely because public employment programs targeted them, while wealthier households led fertilizer adoption.

Geography mattered too. Distance from roads significantly reduced persistent fertilizer adoption and increased non-adoption, underscoring how remoteness and transport costs strangle access to inputs. Non-adopters of crop rotation were located closer to major roads but nearer national borders, hinting that proximity alone does not guarantee adoption when markets, extension infrastructure, and services remain out of reach. The authors also found evidence of substitution rather than synergy in some contexts: manure use was negatively associated with persistent adoption of rotation and fertilizer, suggesting that resource-constrained farmers relying on organic inputs may feel little need for purchased ones.

The policy implications are direct. Because adoption is dynamic and practice-specific, the authors argue, programs must move beyond celebrating initial uptake and instead build the institutional scaffolding, reinforced extension services, accessible rural finance, and better market connectivity, that keeps practices in place year after year. Interventions should be tailored to the resource profile of each practice: credit for capital-intensive inputs, labor support and community programs for conservation works, and integrated packages where practices complement one another. In a country where agriculture anchors rural livelihoods and climate shocks are intensifying, the study’s central insight is quietly radical: resilience is not built in a single planting season, but in the patient accumulation of sustained, supported practice.

Subject of Research: Determinants of smallholder farmers' adoption trajectories of sustainable agricultural practices in Ethiopia

Article Title: Adoption trajectories of sustainable agricultural practices for climate resilience in Ethiopia

Article References: Belay, F., Barau, A. S., Gemechu, A., & Oljira, A. (2026). Adoption trajectories of sustainable agricultural practices for climate resilience in Ethiopia. Discover Agriculture, 4(1), Article 314. https://doi.org/10.1007/s44279-026-00790-2

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00790-2

Keywords: sustainable agriculture, Ethiopia, smallholder farmers, adoption trajectories, climate resilience, crop rotation, chemical fertilizer, soil and water conservation, panel data, multinomial logit, credit access, extension services

The stakes are high. Maize is the cheapest source of calories among Ethiopia’s major cereals, dominating household diets and providing roughly twice as many calories per dollar as other staples. Yet Ethiopian farmers harvest only about 30 percent of the crop’s potential yield, held back by limited access to improved technologies, market imperfections, liquidity constraints, and technical inefficiency on the farm itself. Earlier efficiency studies have estimated efficiency gaps of 14 to 28.3 percent, meaning producers lose more than a quarter of their potential output to avoidable waste. Closing even part of that gap could translate into meaningful gains for food security across the region.

Cluster farming, introduced nationally through the agricultural commercialization cluster initiative in 2010, is designed to overcome precisely these barriers. The approach groups farmers geographically around a commodity’s production potential and encourages them to coordinate land preparation and planting, share improved seeds, fertilizers, and crop protection chemicals, and receive concentrated support from extension agents. The idea is to shift smallholders from subsistence production toward market-oriented farming by pooling resources and standardizing agronomic practice. In principle, a farmer embedded in a cluster can apply recommended input rates, time sowing within a narrow window alongside neighbors, and tap into collective bargaining power that an isolated plot owner cannot.

To test whether the strategy actually improves efficiency rather than merely raising yields, the researchers surveyed 421 randomly selected smallholder maize farmers during the 2021 production season across the Halaba, Wolaita, Gamo, and Gofa zones. Half of the sample, 206 farmers, produced maize within clusters, while 215 farmed outside them. The team used a two-stage sampling design, first selecting six districts with strong maize potential, three cluster districts and three non-cluster districts, and then randomly drawing households from kebeles within each. A structured questionnaire captured everything from household demographics and land use to input quantities, agronomic practices, and institutional variables such as credit access and extension contact.

The analytical machinery behind the study is where its technical rigor lies. The researchers estimated a stochastic frontier model using a flexible translog production function, chosen over the more restrictive Cobb-Douglas form after a likelihood ratio test decisively rejected the simpler specification. Because smallholder farmers operate under wildly varying socioeconomic and institutional conditions, the team employed a heteroscedastic frontier model, allowing the variance of inefficiency itself to depend on factors such as cluster participation, oxen ownership, income, and extension contact. Ignoring this heterogeneity, the authors note, can seriously bias efficiency estimates. Inputs and outputs were scaled by their sample means so that first-order coefficients could be read directly as elasticities evaluated at the mean.

The frontier results paint a clear picture of what drives maize output. Land is by far the most responsive input, with an estimated output elasticity of 36 percent, followed by labor at 27 percent and fertilizer at 16 percent. Oxen power and seed contributed smaller elasticities of 8 and 6 percent respectively, and the returns to scale estimate of 0.81 indicated decreasing returns overall. Notably, the second-order effect of fertilizer significantly increased returns to output, which the authors attribute to the very low baseline application of inorganic fertilizer, itself a consequence of liquidity and institutional constraints. The second-order terms for oxen and labor were negative, signaling diminishing returns as these inputs intensify.

The headline finding is the efficiency gap between the two groups. Farmers producing maize in clusters achieved an average technical efficiency of 74 percent, while non-cluster farmers reached only 60 percent. A kernel density analysis of the efficiency distributions confirmed that cluster farmers’ scores were skewed toward higher values. Because farmers who join clusters may differ systematically from those who do not, the team went a step further, applying an endogenous switching regression model that accounts for both observable and unobservable differences between participants and non-participants. The frequency of extension contact served as the instrumental variable identifying the model, since extension agents actively promote cluster participation but do not directly determine efficiency.

The counterfactual analysis sharpened the conclusion considerably. Cluster farmers would have lost 18 percent of their technical efficiency had they not participated in cluster production, the average treatment effect on the treated. Conversely, non-cluster farmers would have gained 33 percent in efficiency had they joined, the average treatment effect on the untreated. In other words, the farmers most likely to benefit from clustering are precisely those currently standing outside it. The first-stage probit model, which fit the data well, identified male household headship, oxen ownership, frequent extension contacts, greater market distance, credit access, and household income as significant drivers of cluster participation, with the market distance result suggesting that farmers far from markets see cooperation as a way to cut transaction costs.

The study’s authors argue that the findings carry direct policy weight for a region where land fragmentation is more severe than elsewhere in Ethiopia. They recommend that policymakers and development organizations promote clustering in crop production while addressing the socioeconomic factors that gate participation, including gender gaps in access to extension advice and liquidity constraints that limit input purchase. Strengthening the improved seed system, modernizing extension through digital communication technologies, and promoting climate-smart practices are flagged as complementary priorities. The authors also emphasize that collective action among farmers, government, the private sector, and donors will be essential to scale the model.

For a country where agriculture anchors employment and output but productivity remains stubbornly suboptimal, the message from southern Ethiopia is unusually concrete: farming together measurably reduces the waste that has long eroded smallholder potential. With maize efficiency gaps of this magnitude, the difference between a 60 percent and a 74 percent efficient harvest is not an academic abstraction but the equivalent of several additional weeks of food for a farming household. If the cluster approach can be extended to the non-participants who stand to gain the most, the study suggests, Ethiopia’s staple grain system may be able to squeeze far more nutrition and income from the same scarce land.

Subject of Research: The effect of maize cluster farming on the technical efficiency of smallholder farmers in southern Ethiopia

Article Title: Effect of maize cluster farming on smallholder farmers’ technical efficiency: evidence from Southern Ethiopia

Article References: Fola, M., Tsegaye, G., Zawde, S., & Matsalo, M. (2025). Effect of maize cluster farming on smallholder farmers’ technical efficiency: evidence from Southern Ethiopia. BMC Agriculture, 1(1), Article 4. https://doi.org/10.1186/s44399-025-00006-w

Image Credits: AI Generated

DOI: 10.1186/s44399-025-00006-w

Keywords: cluster farming, maize, technical efficiency, smallholder farmers, Ethiopia, stochastic frontier analysis, endogenous switching regression, agricultural policy, land fragmentation, extension services, food security, agricultural economics

Climate-smart agriculture, promoted by governments and development agencies worldwide, is a package of farming practices designed to sustainably raise productivity, build resilience to climate shocks and, where possible, reduce greenhouse gas emissions from farming itself. Ghana has even built the approach into national policy through its Climate-Smart Agriculture and Food Security Action Plan. Yet adoption among smallholder farmers remains stubbornly low. Previous research pointed to the heavy investments in land, labour and financial capital that many of these practices demand, but few studies had systematically sorted practices by their dominant resource requirement and then traced how that requirement shapes who adopts what.

Jonathan Atta-Aidoo and Philip Antwi-Agyei, both of Kwame Nkrumah University of Science and Technology, tackled that gap with a survey of 350 smallholder farmers in seven communities across the North East and Upper East Regions of Ghana, an area within the Sudan Savannah agroecological zone where a single rainy season runs from May or June to September or October. The districts chosen, West Mamprusi Municipality, Bongo District and Bolgatanga Municipality, are highly exposed to droughts and floods and depend heavily on rain-fed farming. Interviews, conducted in the local Gurne and Mumprusi languages between August and September 2021, lasted about 50 minutes each and covered household characteristics, farm features, institutional access, experience of climatic shocks, behavioural measures and the use of twelve distinct climate-smart practices.

The analytical core of the study is a typology. The researchers assigned each of the twelve practices to the single most essential resource without which adoption would be impossible, producing three categories: land-intensive practices that require farmers to expand their cultivated area, labour-intensive practices that demand additional hands, and finance-intensive practices that call for extra capital. The authors acknowledge the limitation of forcing each practice into one box, since many practices carry multiple resource demands, but argue that identifying the binding constraint is what matters for policy.

To model adoption, the team turned to econometric tools built for interdependent decisions. Because farmers can and do adopt practices from several categories at once, a univariate approach would discard information about how choices move together. The multivariate probit model allowed the researchers to estimate the factors driving adoption of each resource-based category simultaneously, while a multivariate ordered probit model, estimated through a composite marginal likelihood approach, captured how deeply farmers engage within each category, from adopting a single practice to adopting three or more. Positive correlations among the error terms across categories revealed that farmers treat the three typologies as complements rather than substitutes, mixing practices to maximise their synergistic effects.

The headline numbers are striking. About 80 percent of surveyed farmers had adopted at least one labour-intensive practice, roughly 73 percent had taken up at least one land-intensive practice, but only around 55 percent had adopted any finance-intensive practice. The depth of adoption tells an even sharper story: roughly 23 percent of farmers had adopted all four labour-intensive practices, compared with about 3 percent for land-intensive practices and less than 1 percent for finance-intensive ones. In other words, farmers in these communities will happily invest sweat, and will often find ways to work more land, but they balk when a practice demands cash.

Gender emerged as a decisive fault line, but only where money was concerned. The study found that gender played no significant role in the adoption of land-intensive or labour-intensive practices, a result that challenges assumptions that women’s limited control over land automatically blocks their uptake of land-hungry techniques. Women, despite shouldering heavy domestic workloads, evidently find ways to supply the labour these practices require. Finance-intensive practices were a different matter: female farmers were about 36 percent less likely to adopt them than their male counterparts, a disparity the authors attribute to women’s weaker control over financial resources. Education partially offset this gap, with women who had completed primary school or more being substantially more likely to invest in capital-hungry practices.

Institutional factors ran through the results like a common thread. Access to extension services raised the likelihood of adopting land-intensive practices by about 81 percent and finance-intensive practices by a similar margin, suggesting that expert advice reduces the perceived risk of committing scarce resources. Access to input markets boosted adoption across all three categories, and secure land tenure increased the odds of adopting labour-intensive practices by about 39 percent, apparently because farmers want assurance that their investments will not be lost before committing extra money to hired labour. Membership in farmer-based organisations helped women in particular, since such groups often organise rotational labour-sharing arrangements that ease the burden of labour-intensive techniques.

Climate shocks shaped behaviour in ways that varied by shock type and by gender. Farmers who had experienced drought were about 52 percent more likely to adopt land-intensive practices and about 67 percent more likely to adopt finance-intensive ones, with the effect especially strong among women, plausibly because technologies such as drip irrigation and drought-tolerant crops promise output even when rain fails. Flooding told the opposite story for some practices: women who had experienced floods were about 51 percent less likely to adopt finance-intensive practices, reflecting risk aversion about losing farm investments, while expanding farmland in flood-prone areas raises the very exposure farmers fear. Intriguingly, subjective norms, the social pressure farmers feel from their communities, reduced male farmers’ likelihood of adopting finance-intensive practices by about 37 percent, indicating that peer pressure can backfire rather than nudge men toward new technologies.

The study’s policy implications are concrete. The authors recommend expanding extension services, potentially through mobile delivery, pursuing land reforms that remove cultural tenure insecurities, and organising agricultural fairs that connect input dealers directly with farmers. Most pointedly, they call for gender-specific interventions, including credit facilities and extension services tailored to women, to close the resource gap that keeps female farmers out of finance-intensive practices. District-level differences also mattered, with farmers in the more urbanised municipalities adopting at higher rates than those in rural Bongo, hinting that infrastructure and market proximity shape who can respond to climate change. As climate projections darken for West Africa, the lesson from northern Ghana is that helping farmers adapt is less about inventing new practices than about matching support to the specific resource, whether land, labour or capital, that each practice demands.

Subject of Research: Determinants of climate-smart agriculture adoption by resource requirement among smallholder farmers in rural Ghana

Article Title: Climate-smart agriculture adoption in rural Ghana: do resource requirements matter?

Article References: Atta-Aidoo, J., & Antwi-Agyei, P. (2025). Climate-smart agriculture adoption in rural Ghana: do resource requirements matter?. BMC Environmental Science, 2(1), Article 4. https://doi.org/10.1186/s44329-025-00018-6

Image Credits: AI Generated

DOI: 10.1186/s44329-025-00018-6

Keywords: climate-smart agriculture, smallholder farmers, Ghana, Sudan Savannah, gender disparities, land tenure security, extension services, climate shocks, multivariate probit, adoption determinants, food security, resource requirements

Woody species, the trees and shrubs that dot farmlands, home gardens, and communal hillsides, perform functions far beyond their appearance. They stabilize soils against erosion on steep slopes, regulate local water cycles, provide fodder for livestock during dry seasons, supply fuelwood and construction material, and harbor the biodiversity that underpins resilient agricultural ecosystems. In a country where deforestation and land degradation have progressed relentlessly over recent decades, understanding why farmers conserve or clear these species is not an academic question. It is central to any credible strategy for restoring degraded landscapes and sustaining rural incomes. Yet, as the authors note, detailed information on farmers’ perceptions and the factors driving conservation behavior has been strikingly scarce for Enemay District.

To fill that gap, the researchers designed a study combining quantitative rigor with qualitative depth. Between January and June 2022, they surveyed 235 households selected through systematic random sampling from three kebeles, the smallest administrative units in Ethiopia, chosen purposively to represent the district’s agroecological diversity. Semi-structured questionnaires formed the backbone of the data collection, but the team also walked the fields with farmers, conducted focus group discussions, and interviewed key informants, including elders and local experts, to capture nuances that numbers alone cannot convey. Quantitative responses were analyzed with descriptive statistics and binary logistic regression, a statistical technique that estimates how each factor changes the odds of a farmer adopting conservation practices, while qualitative material was examined thematically.

The headline finding is stark. When asked about the current state of woody species conservation in their locality, 77.8 percent of respondents expressed negative perceptions, signaling a widespread belief that trees and shrubs are being lost or inadequately protected. This pessimism matters because perceptions often precede action: farmers who believe that woody vegetation is disappearing may either redouble their planting efforts or, conversely, conclude that individual action is futile. The study does not merely document this sentiment; it probes what lies beneath it, linking negative assessments to observable pressures such as agricultural expansion, fuelwood harvesting, and free grazing, dynamics familiar across much of the Ethiopian Highlands where farmland intensification competes directly with woody cover.

What are farmers actually doing on the ground? The most frequently reported conservation practice was plantation establishment, cited by 15.7 percent of respondents, followed closely by home garden agroforestry at 15.3 percent. Enforcement of community conservation laws, a customary mechanism by which local rules penalize unauthorized tree cutting, was reported by 11.5 percent, while parkland agroforestry, the deliberate retention of scattered trees within cropland, accounted for 9.4 percent. The relatively low percentages are telling. Even the most common practices are adopted by only a small minority of households, suggesting that conservation in Enemay remains an exception rather than a norm. Home gardens and parklands, both traditional agroforestry systems, nonetheless emerge as the principal arenas where farmers and woody species coexist productively.

The analytical core of the study lies in its logistic regression results, which identify the variables that significantly influence whether farmers engage in conservation practices. Eight factors emerged as statistically significant at the five percent level: gender, age, education level, farming experience, access to extension services, access to credit, occupation type, and the distance of farmland from the homestead. Each of these tells a story. Gender differences may reflect the gendered division of labor and control over land and tree resources in rural Ethiopia. Age and farming experience plausibly shape both ecological knowledge and long-term planning horizons. Education can enhance awareness of the benefits trees provide, while occupation type determines how central farming, and therefore on-farm woody resources, are to a household’s livelihood.

Two institutional variables deserve particular attention because they are the levers most amenable to policy intervention. Access to extension services, meaning contact with government agricultural advisors who can disseminate technical knowledge about tree planting and management, significantly increased the likelihood of conservation practice. Access to credit mattered as well, presumably because planting and protecting trees requires upfront labor and inputs while the benefits, whether fruit, fodder, or timber, accrue years later. Households with financial breathing room can afford to invest in a resource whose payoff is delayed. Farmland distance, meanwhile, captures a simple logistical truth: fields far from the homestead receive less daily attention, making it harder to establish seedlings, protect them from livestock, and monitor them over time.

The implications of these findings extend well beyond a single district. If gender, education, extension contact, and credit access systematically shape who conserves trees, then conservation programs designed around blanket prescriptions are likely to miss their targets. The authors argue that improving farmers’ awareness of the value of woody species, strengthening locally relevant extension services, and addressing the socioeconomic constraints that limit household investment in trees could together transform conservation outcomes. In practical terms, this means extension systems that speak to the specific species and farming systems of the highlands, credit products tailored to the long gestation period of tree-based investments, and attention to the particular barriers faced by women farmers, who may lack the land tenure or labor access needed to plant and protect trees.

The study also underscores the value of community-level institutions. The enforcement of community conservation laws, reported by more than one in ten respondents, represents an indigenous governance mechanism in which social norms and local sanctions regulate tree harvesting. Such customary arrangements can be remarkably effective and cost-efficient, but they are vulnerable to erosion as communities modernize and as formal and informal authority structures shift. Integrating these local institutions with formal conservation policy, rather than replacing them, may offer a path toward scaling up woody species protection across the Amhara region and beyond.

Ultimately, the research from Enemay District delivers a message that resonates far beyond Ethiopia: the survival of trees in agricultural landscapes is decided not by foresters or policymakers but by thousands of individual farming households making daily decisions under constraint. Where those constraints bind tightly, through poverty, inattention from extension agents, or fields too distant to tend, woody species decline. Where they are loosened, through knowledge, finance, and supportive institutions, farmers become the most effective conservationists of all. As restoration initiatives across Africa set ambitious targets for tree cover, this study is a reminder that the roots of success lie in the socioeconomic soil of the farms themselves.

Subject of Research: Farmers' perceptions and socioeconomic determinants of woody species conservation in Enemay District, northwest Ethiopia

Article Title: Factors influencing farmers’ perceptions and practices in woody species conservation in Enemay District, Northwest Ethiopia

Article References: Belachew, K. G., & Beyene, B. T. (2026). Factors influencing farmers’ perceptions and practices in woody species conservation in Enemay District, Northwest Ethiopia. Discover Sustainability. https://doi.org/10.1007/s43621-026-04352-w

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04352-w

Keywords: agroforestry, woody species, conservation, Ethiopia, farmers' perceptions, logistic regression, extension services, home gardens, parkland agroforestry, sustainable land use, rural livelihoods, highlands