This study examines maize commercialisation intensity, determinants, and the major constraints confronting smallholder maize farmers in Tolon District of Northern Ghana. A cross-section of 125 maize farmers was selected through a multistage sampling procedure. The study measures commercialisation intensity using the Household Maize Commercialisation Index and employs a fractional logit model with robust standard errors to identify associated factors. The constraints of farmers were analysed through the Friedman test and Kendall’s coefficient of concordance. The results showed that 52.5% of the maize harvested had 70.5 medium-level market participants. Commercialisation intensity was positively correlated with education, household size, area cultivated with maize and access to market information. But negatively correlated with gender, farming experience, distance to market, transportation costs and access to credit. The farmers perceived low output prices, high production costs, and price fluctuations as three major challenges with substantial agreement. The study recommends improving access to farm inputs, strengthening maize markets and price information systems to expand collective marketing arrangements. These measures can improve the profitability and sustainability of smallholder maize commercialisation in Northern Ghana. Read more

Maize production plays a critical role in enhancing food security, income generation, and rural livelihoods in Kenya. Smallholder farmers contribute a substantial proportion of national maize output, yet their productivity remains below potential due to various socioeconomic constraints. This study examined the socioeconomic factors influencing maize production among smallholder farmers in Bungoma-North Sub County, Bungoma County, Kenya, with a view to enhancing food security. The study was guided by the Theory of Production, which explains how variations in production inputs and resources influence agricultural output. A descriptive survey research design was adopted, targeting smallholder maize farmers within Naitiri, Mbakalo, and Kabuyefwe wards. Data were collected using structured questionnaires and interview schedules and analyzed using descriptive and inferential statistics. The socioeconomic variables examined included credit accessibility, land ownership, labor availability, educational attainment, income levels, and demographic characteristics. Findings revealed that socioeconomic factors significantly influenced maize production among smallholder farmers. Access to credit enhanced the ability of farmers to purchase improved inputs and adopt modern farming technologies. Household income levels positively influenced investment in agricultural production, while educational attainment improved farmers’ capacity to adopt recommended agronomic practices. Land ownership and farm size were also found to significantly affect maize yields due to their influence on production scale and resource allocation. Regression analysis demonstrated that socioeconomic factors significantly predicted maize production outcomes, indicating that improvements in access to financial resources, education, labor, and productive assets could substantially increase maize productivity and strengthen household food security. The study concludes that socioeconomic factors remain fundamental determinants of maize production among smallholder farmers and should be prioritized in agricultural development programs. The study recommends strengthening rural credit systems, promoting farmer education and training, improving access to agricultural inputs, and supporting income-generating activities that enhance farmers’ production capacity. These interventions would contribute significantly to sustainable maize production and improved food security in Bungoma-North Sub County and similar agricultural regions. Read more

The Spodoptera frugiperda (Lepidoptera: Noctuidae), commonly known as the fall armyworm (FAW), is a highly invasive pest that threatens diverse cropping systems worldwide, particularly maize-based systems in sub-Saharan Africa. Intensive reliance on synthetic insecticides has raised concerns regarding human health risks, environmental contamination, and disruption of natural enemies. This study evaluated whether maize intercropping with edible legumes, combined with botanical and biological treatments, could reduce FAW infestation and improve yield under field conditions in five municipalities of Benin during the 2025 rainy season. Nine treatments were tested: (i) maize monoculture with standard agronomic practices (control), (ii) maize–soybean treated with cashew balm, (iii) maize–groundnut treated with BotaniGard 22WP, (iv) maize–cowpea treated with Ricinus communis extract, (v) maize–mucuna treated with Thevetia neriifolia extract, (vi) maize–sorghum treated alternately with cashew balm and BotaniGard 22WP, (vii) maize monoculture treated with TopBio, (viii) maize monoculture treated with salt plus crushed charcoal, and (ix) the Push–Pull technique. Data collected included FAW infestation symptoms, severity scores, larval density, and maize yield. Results indicated that several treatments significantly influenced infestation severity. The untreated control exhibited the strongest effect, increasing by more than fourfold the probability of belonging to a higher severity category (OR = 4.27; 95% CI: 3.25–5.61; p < 0.001). In contrast, the MSB (OR = 1.27; p = 0.092) and MSBB (OR = 0.84; p = 0.22) treatments showed no significant effect on severity. All tested treatments significantly reduced mean larval counts compared with the control plus agronomic practices (p < 0.05). The most pronounced reductions were observed with CS+A (IRR = 0.27), MSBB+A (IRR = 0.29), and MT+A (IRR = 0.37), corresponding to larval decreases exceeding 60%. Notably, MT+A significantly increased maize yield by 0.28 t ha⁻¹ (p = 0.018). Overall, maize intercropping with edible legumes and the Push–Pull strategy significantly reduced FAW infestation compared with maize monoculture. These approaches represent promising, environmentally sustainable components of integrated FAW management in African smallholder systems. Read more

Maize is a staple crop in the region, and its productivity plays a vital role in ensuring food security and improving the livelihoods of smallholder farmers. Despite the potential for high yields, smallholder farmers face challenges in optimizing production due to factors such as limited access to modern farming technologies, insufficient institutional support, and high input costs. This study explores the effect of technological factors, institutional factors and production costs on maize productivity among smallholder farmers in Songea District, Tanzania. The study uses a cross-sectional design collected data from 397 smallholder maize farmers. To address potential endogeneity arising from key variables such as credit access, land size, and fertilizer use, the analysis employs an instrumental variable Two-Stage Least Squares (IV-2SLS) estimation. The IV regression results reveal that both technological and institutional factors play a significant role in improving maize productivity. In the second-stage estimates, land size (p < 0.05) and credit access (p < 0.01) significantly increase productivity, indicating that scale effects and financial access enable farmers to allocate inputs more efficiently. Technological variables including improved seed adoption (p < 0.01), inorganic fertilizer use (p < 0.01), integrated pest management (p < 0.01), and tractor use (p < 0.01) also have strong positive effects on maize output per worker. Institutional factors further contribute to productivity gains. Market access (p < 0.01) and training attendance (p < 0.01) significantly improve output; suggesting that improved information flows and market integration enhance production efficiency. Household labor (p < 0.01), household size (p < 0.01), income (p < 0.01), and favorable rainfall conditions (p < 0.01) also positively influence maize productivity. In contrast, higher fertilizer costs (p < 0.01), pesticide costs (p < 0.01), and seed costs (p < 0.01) significantly reduce productivity, indicating potential inefficiencies in input expenditure and diminishing marginal returns to cost increases. Elasticity estimates from the Cobb–Douglas IV model show that fertilizer use (0.499), land size (0.853), and labor (0.042) positively contribute to maize output. Hypothesis testing confirms diminishing marginal productivity of fertilizer and labor, while land size exhibits constant marginal productivity. The combined elasticity of core inputs suggests that maize production operates under constant returns to scale. Thus, strengthening access to agricultural inputs, credit services, extension programs, and market infrastructure can significantly improve maize productivity and contribute to food security and rural income growth in Tanzania. Read more

While maize is a key crop for national food security and employment, most maize-growing households have small overall average farm sizes and remain highly vulnerable. The overall cultivated area of maize has been increasing, although such growth has not seen a commensurate gain in yields. The government of Uganda is concerned with this situation and has supported farmers with policies and projects to improve maize yields and, thereby, farm incomes. These projects have generated solid evidence showing that the use of improved seed generates higher productivity and financial returns compared with the use of traditional seeds. In spite of this, the use of improved seeds remains low in Uganda. This paper examines the key factors that influence farmers’ decisions to adopt the use improved maize seeds. To address this objective, a large sample of more than 3,600 maize farmers was analyzed using a binary logistic regression model. We found that maize farmers tend to use improved seeds when they also use fertilizers and pesticides, when improved seeds are obtained from the government and/or purchased from reputable agro-input dealers, and when credit services are readily available. However, a key factor that prevents small maize farmers from using improved seeds is their overall level of vulnerability. For the majority the risk is too high; rather than embarking on investing in seeds with a promise of higher net returns, they prefer to continue using the relatively cheaper traditional seeds that reduce the risk of family members experiencing hunger and poverty. The government needs to provide an enabling environment to make agricultural insurance available to small farmers if the use of improved seeds is to be expanded significantly. Read more

This study assessed the effects of climate variability on Maize yield in Wukari Local Government Area of Taraba State between 1999 and 2018. The research design employed in the study was an ex-post facto and analytical design. Hence, the study is a quantitative study that provides statistical data as empirical evidence and produces descriptive and informative sceneries of the topic study. The study employed the use of secondary data, sourced from the Nigeria Metrological Agency (NIMET) and Agriculture Development Program (ADP), Jalingo. Data used in the study were analysed using both descriptive and inferential statistics. The descriptive statistics used in the study were the time series trend analysis, while the inferential statistics employed in the study were the Ordinary Least Square (OLS) regression and correlation analysis. Based on the objectives of this study and data analysis, the results revealed an increasing trend in average annual maximum temperature in the study area, with a 62 percent variability, and a deceasing trend in average annual minimum temperature, with a 44 percent variability. More so, the results revealed an almost uniform but increasing trend in average annual rainfall in the study area between 1999 and 2018, with a 37 percent variability. Furthermore, the results of the trend analysis revealed an increasing trend in maize yield in the study area, with a 50 percent variability. The correlation analysis revealed a non-statistically significant strong positive linear relationship (r = 0.088, sig. 0.712 > 0.05) between the average rainfall and average temperature in the study area. The correlation analysis also revealed a non-statistically significant weak negative relationship (r = -0.072, sig. 0.762 > 0.05) between average rainfall variability and maize yield in the study area. A statistically significant moderate positive linear relationship (r = 0.564, sig. 0.010 < 0.05) was found between average temperature and maize yield in the study area. The linear regression analysis revealed that 33 percent (r-squared: r2 = 0.333) of the variation in maize yield is explained by the variation in rainfall, and temperature between the periods of 1999 and 2018 in Wukari Local Government Area of Taraba State. Hence, rainfall and temperature affect maize yield in the study area by 33 percent. The study thus recommended that farmers should adopt climate change and variability mitigation and adaptive measures. These include the use of resistant and drought tolerant species. In addition, there is need for the Ministry of Agriculture and Taraba State Agricultural Development Programme to educate farmers and farm agents on the realities and effects of climate change and variability, as well as adaptive measures that can be taken. These include better and practicable environmental policies, improved agricultural techniques, and alternative water sources which consist of irrigation farming, and mulching, vis-à-vis creating sustainable food security in the long run. Read more

Fertilizer use supports productivity in arable farming. However, improper use of inorganic fertilizers can harm the environment, accelerate climate change and slow down the achievement of the 13th SDG. This study examined the use of farm demonstration in improving maize farmers’ perception of organic fertilizer use in the five major town in Ikenne LGA of Ogun state. Sixty-five maize farmers were selected for the study using multistage sampling technique. Demonstration plots were established near the farmers to observe agronomic impact of organic fertilizer use. Result showed that respondents’ average farm size was less than 1 hectare, 58% had undulating or sloppy lands which are vulnerable to degradation. Only 22% of the farmers mostly use organic fertilizers which they source from unprocessed animal waste. Only 32% of the farmers are aware of commercially processed organic fertilizers. Using the pair-sample t-test, the perception level of the farmers at the end of the demonstration was significantly higher than before the demonstration (t = 14.87, p <0.05). The study recommended that perception enhancing intervention be introduced to farmers in order to increase the use of clean and safe commercially available fertilizers. Read more

Maize sector in Tanzania is crucial for the country's food security and economic stability. However, farmers face various challenges, including diseases that reduce yields and limited access to information. To address this, an innovative solution called MkulimaGPT has been developed, it is a text and voice chatbot that uses generative Artificial Intelligence (AI) technology and is accessible through WhatsApp. The platform offers real-time agricultural guidance in Swahili, overcoming language and literacy barriers. It is available through a local phone number and designed to cater to Tanzania's digital landscape by utilizing the widespread mobile network. MkulimaGPT blends generative AI's capabilities with the comprehensive agricultural knowledge base, ensuring its advisory services are contextually rich and technically astute. It is an example of inclusive AI and bridges the gap between farmers and extension services, fostering inclusivity for marginalized groups, particularly women, with a commitment to deliver real-time, and actionable insights for Tanzania's maize farming. Read more

Artificial intelligence and deep learning models are utilised in health, IT, animal and plant research, and more. Maize, one of the most widely eaten crops globally, is susceptible to a wide variety of disease that impede its development and reduce its output. The objective of this research work is to develop a deep learning-based model for detection of illnesses affecting maize leaves. Furthermore, the model that has been constructed not only forecasts illness but also furnishes illustrative visuals of leaf diseases, so facilitating the identification of disease types. To do this, a dataset including specified illnesses, including blight, common rust, gray leaf spot, and a healthy leaf, was obtained from Kaggle, a secondary source (Pant village). For data analysis, the cross-platform Anaconda Navigator was used, while the programming languages Python and Jupiter Notebook were implemented. The acquired data was used for both training and evaluating the models. The study presents a novel approach to plant disease detection using the YOLO deep learning model, implemented in Python and associated libraries. The Yolov8 algorithm was employed to develop a maize leaf detection system, which outperformed algorithms such as CNN (84%), KNN (81%), Random Forest (85%), and SVM (82%), achieving an impressive accuracy of 99.8%. Limitations of the study include the focus on only three maize leaf diseases and the reliance on single-leaf images for detection. Future research should address environmental elements like temperature and humidity, include numerous leaves in a frame for disease identification, and create disease stage detection methods. Keyword: Yolov8, Maize Leaf Detection, Machine Learning, Artificial Intelligence, Model. Read more

Sweet maize (Zea mays L. convar. saccharata var. Rugosa) is unique in sweet-taste, and pleasant flavour, hence the need to increase its usefulness and commercial values by enhancing its shelf-life and control of post-harvest losses. This preliminary study therefore simulated the traditional, natural, non-alcoholic, field-corn fermentation method for freshly-harvested yellow sweet corn into ògì, a fermented gruel or porridge food. Lactobacillus casei, Lactobacillus fermentum, Lactobacillus plantarum, and Saccharomyces cerevisiae predominated the naturally fermented ògì sample from sweet maize, with pH of 5.0–5.3. Observable characteristics of yellow sweet maize-fermented ògì were similar to those of yellow, field corn-fermented ògì. Present study is the first to report fermentation of sweet corn to ògì, indicative of sweet maize as a high potential plant substrate for health-promoting and nutritious fermented human diet, especially as prebiotics and probiotics, for lactating nursing mothers, convalescents, aged, and as weaning-food for infants or complementary food for children and adults. Read more