To determine whether a farmer’s accessibility predicts the delivery of extension services, this study used banana Xanthomonas wilt (BXW) disease-management advisory as a typical case with which to collect extension-delivery information from 690 farmers, distinguished by their respective accessibility. Cost–distance analysis was applied to define each farmer’s accessibility. The results revealed that a farmer’s accessibility does not predict extension delivery to that farmer in all forms of the examined extension parameters.

Although the benefits of genetically modified (GM) crops have been well documented, how do farmers manage the risk of new technology in the early stages of technology adoption has received less attention. We compare the total factor productivity (TFP) of cotton to other major crops (wheat, rice, and corn) in China between 1990 and 2015, showing that the TFP growth of cotton production is significantly different from all other crops. In particular, the TFP of cotton production increased rapidly in the early 1990s then declined slightly around 2000 and rose again.

The national assessment of the agricultural innovation system (AIS) in Malawi was conducted using a framework of four types of analyses: functional, structural, capacity and enabling environment analysis. The approach included five case studies that addressed three methods including the use of indigenous methods for fall armyworm (FAW) control in Farmer Field Schools (FFS), livestock transfer programs, and a horticulture marketing innovation platform in Mzimba, Ntchisi, Balaka, and Thyolo districts. 

This report introduces the reader to the concept of agricultural innovation systems (AIS) and the TAP-AIS project being implemented by FAO in nine countries, including Lao People's Democratic Republic (Lao PDR). The results of the AIS assessment for Lao PDR are presented, highlighting key barriers and opportunities for agricultural innovation in the country.

Inefficiencies and imprecise input control in agriculture have caused devastating consequences to ecosystems. Urban controlled environment agriculture (CEA) is a proposed approach to mitigate the impacts of cultivation, but precise control of inputs (i.e., nutrient, water, etc.) is limited by the ability to monitor dynamic conditions. Current mechanistic and physiological plant growth models (MPMs) have not yet been unified and have uncovered knowledge gaps of the complex interplay among control variables.

Improvements in the sustainability of agricultural production depend essentially on advances in the efficient use of nitrogen. Precision farming promises solutions in this respect. Variable rate technologies allow the right quantities of fertilizer to be applied at the right place. This helps to both maintain yields and avoid nitrogen losses. However, these technologies are still not widely adopted, especially in small-scale farming systems. Recent developments in sensing technologies, like drones or satellites, open up new opportunities for variable rate technologies.

The problems of agricultural development for small and medium enterprises (SMEs) are considered. The features of modeling business processes in agriculture are analyzed. A financial decision support system is proposed to increase sustainability and reduce risks in the development of agricultural SMEs. The software modules are based on TEO-INVEST.

Based on GIS technologies, a decision support system (GIDSS) has been developed to remediate agricultural lands in the Bryansk region (Russia) contaminated by 137Cs after the accident at the Chernobyl nuclear power plant. GIDSS is a multilevel system consisting of basic, information and computational layers. GIDSS allows justifying a targeted approach for the remediation of agricultural lands belonging to agricultural enterprises for the production that meets the established radiological requirements for the content of radionuclides.

Decision support systems (DSS) have long been used in research, service provision and extension. Despite the diversity of technological applications in which past agricultural DSS canvass, there has been relatively little information on either the functional aspects of DSS designed for economic decisions in irrigated cropping, or the human and social factors influencing the adoption of knowledge from such DSS.

The impact of global warming on crop growth periods and yields has been evaluated by using crop models, which need to provide various kinds of input datasets and estimate numerous parameters before simulation. Direct studies on the changes of climatic factors on the observed crop growth and yield could provide a more simple and intuitive way for assessing the impact of climate change on crop production.