Women play important roles at different nodes of both agricultural and off-farm value chains, but in many countries their contributions are either underestimated or limited by prevailing societal norms or gender-specific barriers. We use primary data collected in Asia (Bangladesh, Philippines) and Africa (Benin, Malawi) to examine the relationships between women’s empowerment, gender equality, and participation in a variety of local agricultural value chains that comprise the food system.
Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies.
Structural transformation of agriculture typically involves a gradual increase of mean farm sizes and a reallocation of labor from agriculture to other sectors. Such structural transformation is often fostered through innovations in agriculture and newly emerging opportunities in manufacturing and services. Here, we use panel data from farm households in Indonesia to test and support the hypothesis that the recent oil palm boom contributes to structural transformation. Oil palm is capital-intensive but requires much less labor per hectare than traditional crops.
Productivity growth in smallholder agriculture is an important driver of rural economic development and poverty reduction. However, smallholder farmers often have limited access to information, which can be a serious constraint for increasing productivity. One potential mechanism to reduce information constraints is the public agricultural extension service, but its effectiveness has often been low in the past.
Agricultural innovation has played a critical role in the economic transformation of developing East Asian countries over the past half century. The Green Revolution—in the form of modern seed varieties, chemical fertilizers, pesticides, and modern machinery—has contributed to increased crop yields and farm incomes, and decreased poverty across the region. Although policy makers’ traditional focus on expanding and intensifying agricultural production has brought many benefits, the focus on productivity has come at a rising cost.
Agricultural transformation is one of the important factors of rural planning and sustainable land management. There are natural and man-made reasons of this transformation, which brings both positive and negative impacts on the physical environment, food security, and human livelihoods. This study aims to explore a case of land use conversion from traditional agricultural land to the man-made wetland for fisheries in Rajshahi, Bangladesh. The conversion might be a result of purposeful land use and land cover changes to continue agricultural production for the market demands.
In creating a usable Information System (IS), the quality of information is crucial for making the right decisions. Although, many Information Quality (IQ) features have been identified in a broader context, only certain IQ features would become applicable for each domain from the usability perspective. This study focuses on a theoretical analysis to identify the IQ features which would be significant to produce a usable agricultural information system with respect to the developing countries.
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.
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.