The COVID-19 pandemic, surprised many through its impact on the food systems, resulting in collapses in the food production value chains and in the integrated pest disease management sector with fatal outcomes in many places. However, the impact of COVID-19 and the digital experience perspective on Integrating Pest Management (IPM) is still yet to be understood. In Africa, the impact was devastating, mostly for the vulnerable smallholder farm households, who were rendered unable to access markets to purchase inputs and sell their produce during the lockdown period.

Africa accounts for about 7% of the world’s total fish production (FAO 2020). Nile tilapia (Oreochromis niloticus), a native of Africa, is the third-most farmed fish species in the world (FAO 2020) and is consumed worldwide (Asiedu et al. 2015). Because of market demand and the availability of suitable water bodies, Nigeria and Ghana have enormous potential for tilapia aquaculture, particularly in cages (Njoku et al. 2022).
Cage aquaculture refers to rearing captive fish within a floating enclosure in a water body. Typically, the

Smallholder farmers’ decisions to adopt improved varieties are expected to be critically governed by their preferences and willingness to pay for different traits of these varieties. This study examined farmers’ preferences for sorghum (Sorghum bicolor (L.) Moench) variety attributes and estimated their willingness to pay (WTP) for these attributes using choice experiment data from >1,300 sorghum farmers in Tanzania.

This study examined the determinants and impacts of mobile money (MM) usage on maize productivity and poverty likelihood (i.e., the probability of a household falling below the international poverty line at USD 1.9 per capita per day) in the Mbeya Region, Tanzania. The analysis was conducted using the endogenous switching regression (ESR) model on data from a random sample of 1310 households selected from seven districts in the region.

Persistent food insecurity in the global south has triggered calls for sustainable development worldwide. Moreover, more than a quarter of the world's population suffers from micronutrient deficiencies or hidden hunger. The population bulge, declining soil fertility and inadequate/inappropriate use of farm inputs in Sub-Saharan Africa place it in a precarious position. Multi-nutrient fertilizer blends have been mooted as a key innovation in closing yield gaps and boosting food and nutrition security.

Making quality seed of improved legume varieties sustainably available and accessible to farmers in a timely manner and at affordable price is a major challenge in sub-Saharan Africa (SSA). Overcoming this challenge requires collective and long-term action through public–private and development organization partnerships (PPDOPs). The PPDOP model was tested by key seed system actors under the Tropical Legumes (TL) and Accelerated Varietal Improvement and Seed Delivery of Legumes and Cereals in Africa (AVISA) projects from 2008 to 2021.

Analysis of hydro-climatological time series and spatiotemporal dynamics of meteorological variables has become critical in the context of climate change, especially in Southern African countries where rain-fed agriculture is predominant. In this work, we compared modern unsupervised time series and segmentation approaches and commonly used time series models to analyse rainfall regime changes in the coastal, sub-humid and semi-arid regions of Southern Africa.

Field experiments were conducted in two different agroecological locations of Ibadan and Ikenne in Nigeria from August through December during the 2019 and 2020 cropping seasons. The studies were set up to reduce reliance on inorganic nitrogen fertilizer and to embrace the use of nitrogen-fixing bacteria to improve legume production to increase farmers' output and profitability. Ten accessions of the Bambara groundnut (BGN) were used in the trials. Seeds of each BGN accession were coated with each of the following Bradyrhizobium japonicum strains (B.

The quest to transform and hasten the role of smallholder farms in agricultural development and food security through farmer firm linkages has dominated development interventions in low-income countries for several decades. This has mostly been pursued through single- or multi-contract schemes implemented in isolation. Several studies have analyzed the efects of these schemes on smallholder farms with mixed results. A new paradigm is to use Agricultural Public Private Partnership (Ag-PPP) to achieve wider and sustainable impacts.

Climate change-induced heat stress combines two challenges: high day- and nighttime temperatures, and physiological water deficit due to demand-side drought caused by increase in vapor-pressure deficit. It is one of the major factors in low productivity of maize in rainfed stress-prone environments in South Asia, affecting a large population of smallholder farmers who depend on maize for their sustenance and livelihoods.

Context
Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency.
Objective
The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide.
Methods

Brown spot, caused by Bipolaris oryzae, is becoming more prevalent and important in the Philippines but risk assessment is limited. Surveys of 2186 farmers' fields were conducted in the major rice‐growing areas in the country from 2015 to 2018 to analyse the components of production situation (PS) that contribute to the area under the progress curve of brown spot incidence (aBS). Based on lasso regression, the components of PS that influence aBS, in decreasing order, are ecosystem (irrigated vs. rainfed), crop establishment method (direct seeding vs.