Search Results (188)

Understanding how irrigation water management shapes crop performance is critical for improving productivity and resource-use efficiency in peri-urban agriculture. This study investigated the socio-technical factors driving sprinkler system abandonment and assessed how irrigation water variability influences vegetable yield variability at two market gardening sites (Bogdin and 14 Yaar) in Ouagadougou, Burkina Faso. Survey data from 50 farmers and field measurements of soil properties, irrigation water application, and lettuce yield were analyzed using descriptive statistics, Spearman correlations, and principal component analysis. More than 80% of farmers had ceased using the sprinkler system within two years of installation, 76% reported major equipment failures, and 70% expressed willingness to re-adopt an improved system. Irrigation dose and yield showed considerable variability across sites (CV = 20.9–42.3% and 36.4–44.0%, respectively). At 14 Yaar, irrigation dose was strongly associated with yield (r = 0.862, p = 0.006), indicating that uneven water application was a major constraint on productivity. At Bogdin, where irrigation was more uniform, no single soil or water variable dominated yield variability. Although soil fertility variables contributed to multivariate patterns, nutrient–yield correlations were not statistically significant under the available sample size, and their potential influence on yield requires confirmation with larger datasets. Overall, operational constraints, equipment failures, and inadequate support services contributed to sprinkler system abandonment, while variability in manual water application was associated with variability in crop productivity. These findings highlight the need for irrigation strategies that are both technically robust and adapted to farmers’ realities. Full article

The rising global population has heightened food demand, placing pressure on agricultural systems, particularly in water-scarce regions such as South Africa. Smallholder farmers, essential to the sector, face climatic variability and resource constraints, necessitating innovative solutions to enhance sustainability and productivity. This study evaluates unmanned aerial vehicles (UAVs) for generating spatially explicit evapotranspiration (ET) estimates in a small-scale sugarcane field, supporting precision water management. Vegetation indices (VIs) derived from UAV-based multispectral imagery were used to predict actual ET (ET_a) and validated against eddy covariance measurements. Five models were assessed, including Normalised Difference Vegetation Index (NDVI)-based and Enhanced Vegetation Index (EVI)-based approaches. Machine learning was used to relate crop coefficients (K_c) to NDVI, enabling improved estimation. The two-band EVI (EVI2) model achieved the highest accuracy, with an R² of 0.63, an RMSE of 0.67, and an MAE of 0.52. ET-VI approaches, particularly EVI2, require lower data and technical complexity, making them suitable for smallholder systems. However, reducing dependence on in situ data remains essential to improve accessibility of remote sensing approaches for agricultural water management in resource-limited environments. These findings demonstrate the potential of UAV-based ET_a modelling to support field-scale irrigation decision-making while highlighting the need for further refinement to improve operational applicability across diverse smallholder farming contexts and beyond. Full article

This study assesses improved maize variety (IMV) adoption, as well as yield effects among smallholder farmers in Benue State, Nigeria, having implications for sustainable crop management and resource-use efficiency. Benue state is commonly known as the “Food Basket of the Nation,” but the average maize yield remains less than 2 t/ha, compared to 7–10 t/ha when achieved under improved technologies, and it shows a key sustainability challenge for food security and land-use efficiency. With primary cross-sectional survey data from 205 smallholder farmers with 107 adopters and 98 non-adopters, selected across Local Government Area (LGAs) in Benue State, this study adopts Propensity Score Matching (PSM) for controlling selection bias and estimating the Average Treatment Effect on the Treated (ATET). Nearest Neighbour Matching acts as a primary estimator through robustness checks while using Radius and Kernel Matching. However, the logit model shows that IMV is greatly determined by gender, use of fertilizer, formal education, cooperative membership, access to irrigation, and extension contact, highlighting the crucial parts of human capital, complementary inputs, and institutional support in promoting sustainable adoption of technology. Following the control for observable differences across matching, a 0.399 log-unit yield gain was achieved by adopters, which is equivalent to approximately 49% higher output per hectare compared to non-adopters, an effect that is robust throughout alternative matching algorithms, and it surpasses the 38.7% national-level yield increase, indicating a regional sustainability premium in Benue State. The gains in productivity can promote land-use efficiency, decrease pressure for agricultural intensification on vulnerable lands, and enhance the case for integrated crop management. But adoption remains limited by access to quality seeds, complementary inputs, credit, and sustained gender barriers. Improving input supply chains, extension services, and institutional support is therefore crucial for developing productivity, resource-use efficiency, and food security across smallholder farming systems. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article

Greenhouse vegetable cultivation in tropical regions is often affected by high temperature, unstable humidity, and irrigation management problems. This study presents a pilot-scale case study of Green Oak lettuce cultivation using an IoT-based sensor monitoring and automated irrigation control system in Phra Nakhon Si Ayutthaya Province, Thailand. The system used AM2315C, BH1750, NPK, and flow sensors connected to ESP32. Data were transmitted to the ThingsBoard platform for real-time environmental monitoring and irrigation control. The greenhouse temperature averaged 33.21 ± 3.61 °C, while relative humidity averaged 71.55 ± 9.66%. The average daytime light intensity was 16,976 ± 409 lux. Nitrogen (N), phosphorus (P), and potassium (K) concentrations remained within ranges of 62.42–74.57, 76.46–84.30, and 71.46–79.30 mg/kg, respectively. Economic evaluation demonstrated favorable feasibility, with a water use efficiency (WUE) of 0.63 kg/L, return on investment (ROI) of 40%, benefit–cost ratio (BCR) of 1.6, and payback period of approximately 2.5 years. The developed system demonstrates potential for supporting sustainable greenhouse agriculture and contributes to SDG 2, SDG 6, SDG 12, and SDG 13 under tropical agricultural conditions. Full article

Smallholder irrigation schemes in semi-arid Zimbabwe are vital for food security but increasingly constrained by declining soil fertility, inefficient water use, and climate variability. Closing nutrient loops through manure recycling offers a potential pathway to improve system sustainability. This study evaluated the effects of manure recycling on maize grain yield and water productivity (WP) under supplemental irrigation at Silalatshani, Zimbabwe, using APSIM simulations across historical and mid-century climates. Simulations included manure rates (0–10 t ha⁻¹), nitrogen levels (20–150 kg N ha⁻¹), irrigation regimes, and climate scenarios, with improved irrigation guided by smart water management (SWM) tools. Grain yield was significantly influenced by nitrogen, manure, and irrigation (p < 0.05), with strong climate × nitrogen × manure interactions. Yields declined under increasing climate stress, predominantly under unimproved irrigation. Manure improved yield and water productivity, with greatest benefits at 2.5–5 t ha⁻¹, beyond which responses diminished. Water productivity gains were modest and constrained by water availability. Economic benefits were limited to moderate manure rates with adequate nitrogen. Combining moderate manure application with appropriate nitrogen fertilisation and improved water management, supported by SWM tools, provides a reliable and economically viable pathway for enhancing productivity, profitability, and resilience under climate variability. Full article

Large portions of rural population in South Africa lack access to basic water and sanitation. This advocates for urgent interventions in support of water supply. This study assessed the performance of solar-powered groundwater pumping systems established at nine pilot sites in rural areas of Greater Giyani Municipality (Limpopo, South Africa). Performance assessment indicators, namely weather, groundwater abstraction, power supply, water supply, water quality, number of beneficiaries and farm productivity, were monitored (2023–2024). Increased groundwater abstraction reduced groundwater levels by 0.4–11 m, depending on the monitored borehole. This was replenished by above-average rainfall in 2023 (≈650 mm). Power supply and pump discharge rates were stable with generally low fluctuations at recommended pumping rates (0.5–2.0 L s⁻¹). Groundwater quality was generally fit to marginal for irrigation and drinking. High levels of NO₃⁻ and total organic carbon, especially in the proximity of villages, mandated the installation of mini water treatment plants for drinking water. The implementation of solar-powered groundwater pumping schemes was generally successful, with more than 5000 villagers benefiting directly from the interventions, whilst smallholder farms turned into commercial and financially viable enterprises. Long-term monitoring of bio-physical and socio-economic drivers is essential to ensure long-term sustainability of the solar-powered groundwater pumping systems. Full article

This study developed and evaluated a cloud-based smart irrigation platform (DSmart Farming) integrating low-cost sensors and IoT technology for automated irrigation control in community greenhouses of Puen Jai Insee, organic group in Sa Kaeo Province. The system combined soil moisture, air temperature, and relative humidity sensors, with a LoRa32-based control unit in each greenhouse and a central web-based management application linked to a MariaDB database on a cloud server. Five vegetable crops, including cherry tomato, broccoli, cabbage, Chinese kale, and kale, were grown over two distinct seasons under four irrigation strategies in a completely randomized design with three replications: three smart irrigation treatments based on soil moisture thresholds (on/off at 40/50%, 45/55%, and 50/60%) and a farmer-managed conventional irrigation control. The smart irrigation system maintained root-zone moisture within the target range (approximately 50–60%) and moderated greenhouse microclimate, preventing daytime temperatures from exceeding 40 °C, in contrast to 40–45 °C peaks in the conventional greenhouses. Across crops, smart irrigation increased yields by 20–29% while reducing water use by 41–60% compared to conventional practice, leading to income increases of 20–56%, depending on the crop. Bacterial soft rot caused by Pectobacterium carotovorum subsp. carotovorum occurred only under conventional irrigation, whereas no soft rot or other major diseases were detected in smart-irrigated greenhouses. These results demonstrate that the DSmart Farming system can enhance water use efficiency, avoid disease incidence, and improve the productivity and profitability of organic greenhouse vegetable production in water-limited smallholder systems. Full article

Climate change poses a significant threat to water resources and agricultural sustainability, particularly in semi-arid and socio-economically vulnerable regions such as South Africa. This review synthesizes empirical, modelling, and policy-based evidence on the impacts of climate change on water availability, crop production, and adaptation strategies in the country, drawing on approximately 162 peer-reviewed studies and institutional reports published between 2010 and 2025. The findings indicate that rising temperatures, shifting rainfall patterns, and an increasing frequency of extreme events, such as droughts and floods, are intensifying water stress and disrupting agricultural systems. Hydrological models consistently project declines in runoff, soil moisture, and streamflow, while crop simulation models predict reductions in the yields of major staple crops, including maize, wheat, and sorghum, particularly under high-emission scenarios. Although localized improvements in water availability and crop productivity may occur, these tend to be limited and highly context-specific. In response, South Africa has implemented a range of adaptation strategies, including climate-smart agriculture, water-efficient irrigation, ecosystem-based approaches, and policy-driven interventions. However, their effectiveness remains constrained by institutional fragmentation, limited financial capacity, and persistent socio-economic inequalities, particularly among smallholder farmers. The review underscores the need for integrated, inclusive, and context-specific adaptation strategies that strengthen governance, enhance the science–policy interface, and improve access to climate finance. The insights provided offer valuable guidance for advancing climate resilience in South Africa and other vulnerable regions across the Global South. Full article

Rainfed agriculture is the most common type of agriculture in South Africa among smallholder farmers, accounting for the majority of the arable land. In a country with so much potential, only about 8% of the arable land is under irrigation. In response, the South African post-apartheid government has invested in the establishment of irrigation schemes in rural provinces such as the Eastern Cape to promote the sustainability of smallholder farming systems. Despite these efforts, the participation of farmers in these schemes remains low. This study investigated socioeconomic and environmental factors that affect farming households’ level of participation in irrigation schemes and intensity. Cross sectional data was collected from 209 households using a multi-stage sampling procedure. Descriptive statistics was used to analyse the socio-economic and environmental factors. A double hurdle model was used to analyse both participation in irrigation and the intensity of participation. The study results reveal that agriculture is largely practised by elderly farmers with an average age of 54 years and largely female-dominated (58%). On average, farmers have 7.5 years of schooling and 12 years of farming experience. Econometric findings demonstrate that participation is significantly influenced by market access, whereas participation intensity is driven by market access, market information and the level of education. The study recommends strengthening gender-targeted agricultural support systems, improved water access through expanded and well-maintained irrigation infrastructure and improving market access. In addition, enhanced extension training support and youth-focused agricultural programmes are required to build productive capacity and ensure the long-term sustainability of irrigation schemes. Full article

Water scarcity is a global phenomenon, and Pakistan is no exception to it. This study aims to assess the techno-economic efficiency of the irrigation system for guava orchard and melon crop in the Hafizabad District of Punjab province in Pakistan. The study has employed efficiency theory for a comparative analysis of modern and high-efficiency irrigation methods in contrast to old traditional methods of irrigation to estimate differentiating impacts on technical efficiency (TE), economic efficiency (EE), water productiveness, and crop yield. The mixed method approach is exercised on data collected from 108 stratified farmers (large, medium and smallholders) using structured surveys and qualitative insights. Beta-regression models using Cauchit link function are applied to translate determinants of TE/EE by taking into account predictor factors such as farming experience, operational costs and water productivity. Results show that solar irrigation systems have significantly better performance than the conventional system by having better TE and EE scores than conventional system performance. Farming experience and water productivity also have positive effects on efficiencies. Results also show that solar systems increase water productivity, lower costs and increase guava and melon productivity to a significant extent, which in turns aid in reducing the effects of salinity and evaporation in arid conditions. The overall finding supports and emphasizes solar’s supremacy for sustainable horticulture. Findings highlight the importance of incentivizing solar adaptation and agrivoltaic integration in Pakistan to ensure sustainable agriculture in water-stressed areas such as Punjab for food security and resource conservation for the production of guava and melons. Full article

South African irrigation schemes face critical challenges of water scarcity, infrastructure deterioration, and limited monitoring capacity, threatening agricultural productivity and food security. This scoping review systematically analyses 59 peer-reviewed publications (2000–2025) on sensor-based and acoustic irrigation monitoring technologies in South Africa, using transformer-based natural language processing (Sentence-BERT embeddings), unsupervised Machine Learning (UMAP dimensionality reduction, HDBSCAN clustering), and geospatial mapping applied to literature retrieved from Web of Science and Scopus. Results show that water quality monitoring (42.4% of studies) and remote sensing (25.4%) dominate the national research landscape, while soil moisture sensing and modelling remain comparatively limited. Notably, no peer-reviewed studies applying acoustic monitoring technologies to irrigation were identified, representing a critical gap despite proven international applications for leak detection (95–98% accuracy), widespread infrastructure aging (over 50% of schemes exceeding 30 years), and reported water losses of 30–60% in poorly managed systems. Reported experimental water savings range from 15% to 30%, yet applications remain largely confined to pilot-scale implementations concentrated within a limited number of Water Management Areas. Persistent adoption barriers include infrastructure unreliability, financial inaccessibility, limited digital literacy, and weak institutional coordination. The review recommends: (i) expanding research coverage across underrepresented regions and Water Management Areas; (ii) strengthening extension support and technical training to enable broader adoption; and (iii) integrating low-cost sensor networks with predictive, data-driven irrigation advisory systems. These priorities aim to support scalable, context-sensitive irrigation modernisation under increasing water scarcity pressures. Full article

Smallholder farming remains a critical livelihood source for rural communities in South Africa, particularly in the Eastern Cape Province. However, cropland abandonment has become an escalating concern, undermining food security, household incomes, and the long-term sustainability of agricultural systems. This study assessed the socio-economic and climate-related factors influencing cropland abandonment in the former homelands of Transkei. A mixed-methods approach was used, combining a quantitative survey, a qualitative focus group discussion, and a key informant interview. Data were analysed using descriptive statistics, a double-hurdle model, and thematic analysis. The descriptive results revealed that the average respondent was 57 years, with a predominantly male majority (57.47%), a primary education (40.27%), and a mean average household size of 5.4. About 51.58% of household heads were married and 48.42% were single, with a mean household income of R63 155 (3680.26 USD). The econometric results from the first hurdle model indicated that education level, farming experience, rainfall variability, access to irrigation, and off-farm income significantly influenced the decision to abandon cropland. The second hurdle model demonstrated that the extent of cropland abandonment was shaped by labour availability, access to credit, rainfall patterns, cooperative membership, and farming experience. The study concluded that cropland abandonment in the former Transkei was influenced by different factors. Therefore, the study would recommend targeted policy interventions that strengthen human capital, improve access to agricultural support services, and promote youth participation and collective farming structures to revitalise smallholder agriculture and enhance rural food security. Full article

The impact of climate change (CC) includes a decline in agricultural production due to crop damage caused by flooding and drought, which destroys crops before harvest, particularly in small-scale irrigation areas. This has led farmers to look for alternative irrigation methods, i.e., groundwater through dug-wells. However, the volume of water obtained through dug-wells is limited. This has led farmers to select the crops they would cultivate. This study aimed to examine the factors that influence farmers in selecting the crops to be cultivated through multinomial logistic regression (MLR). A total of 118 farmers in Jember and Lumajang were randomly selected and interviewed regarding the use of wells and the selection of crops to be cultivated. The dependent variables consist of three crop pattern categories. The results showed that water access variables—particularly well depth, pumped water volume, pipe length, and pump power—significantly influence crop pattern selection (p < 0.01). Farmers adopting diversified crop patterns (food-other and mixed crop pattern) extracted substantially higher groundwater volumes (>76,659 m³ ha⁻¹ annually) and relied on deeper wells (>90 m) compared with the food-crop-dominated pattern. In contrast, water-use-efficient strategies were characterized by lower extraction volumes (<56,755.99 m³ ha⁻¹ annually), longer distribution pipes, and shallower wells (<90 m). Future research should examine the impacts of CC on aquifer depletion and the consequent implications for agricultural activities. Full article

Climate variability threatens smallholder maize production in semi-arid Southern Africa, necessitating accurate irrigation management. We developed an Earth Observation–machine learning framework integrating Sentinel-1 SAR, TU Wien retrievals, and meteorological data to generate daily 10 m resolution root-zone soil moisture estimates (0–100 cm) for South Africa’s Vhembe District (2017–2022). Five algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Multivariate Adaptive Regression Splines (MARS)—were calibrated using ~50,000 observations from two monitoring stations across six depths and five growing seasons. RF and XGBoost achieved highest accuracy (R² = 0.96–0.97, RMSE < 0.025 cm³/cm³), detecting critical irrigation thresholds (management allowable depletion = 0.23 cm³/cm³, field capacity = 0.35 cm³/cm³) with operational precision (nRMSE < 0.05). Depth-stratified validation revealed strong SAR surface correlations (r = 0.84–0.85 at 10 cm) declining systematically with depth (r < 0.2 below 40 cm), confirming ML models integrate satellite observations at shallow layers with meteorological gap-filling at depth. District mapping showed 79–94% of maize areas required irrigation during dry years (2017–2019, 2021–2022) versus 32% in wet 2020–2021. The framework provides a transferable pathway for precision irrigation in smallholder systems, pending vegetation-corrected retrievals and expanded validation. Full article