Natural resource management practices, such as the System of Rice Intensification (SRI), have been proposed to tackle agricultural challenges such as decreasing productivity growth and environmental degradation. Yet, the benefits of system technologies for farmers are often debated. Impacts seem to be context-specific, which is especially relevant in the small farm sector with its large degree of agroecological and socioeconomic heterogeneity. This was not always considered in previous research.

The recent proliferation of mobile phones in rural Africa has also led to increased interest in mobile financial services (MFS), such as mobile money and mobile banking. Such services are often portrayed as promising tools to improve agricultural finance, especially among smallholders who are typically underserved by traditional banks. However, empirical evidence on the actual use of MFS for agricultural activities is thin. Here, we use nationally representative data from Kenya to analyze the use of mobile payments, mobile savings, and mobile credit among the farming population.

Indicator-based tools are widely used for the assessment of farm sustainability, but analysts still face methodological and conceptual issues, including data availability, the complexity of the concept of sustainability and the heterogeneity of agricultural systems. This study contributes to this debate through the illustration of a procedure for farm sustainability assessment focussed on the case study of the South Milan Agricultural Park, Italy. The application is based on a set of environmental, social and economic indicators retrieved from the literature review.

Mobile phone based money services have spread rapidly in many developing countries. We analyze micro level impacts using panel data from smallholder farmers in Kenya. Mobile money use has a large positive net impact on household income. One important pathway is through remittances, which contribute to income directly but also help to reduce risk and liquidity constraints, thus promoting agricultural commercialization. Mobile money users apply more purchased inputs, market a larger proportion of their output, and have higher farm profits.

This publication is based on invited papers presented at the conference "Agricultural Biotechnology in Developing Countries: Towards Optimizing the Benefits for the Poor", held in November 1999 at the Center for Development Research (ZEF), University of Bonn. The conference was convened in collaboration with the International Service for the Acquisition of Agri-biotech Applications (ISAAA), Hoechst Schering AgrEvo GmbH (now Aventis CropScience) and the German Foundation for International Development (Deutsche Stiftung fUr internationale Entwicldung - DSE).

Enhancing the diversity of agricultural production systems is increasingly recognized as a potential

means to sustainably provide diversified food for rural communities in developing countries, hence

ensuring their nutritional security. However, empirical evidences connecting farm production

diversity and farm-households’ dietary diversity are scarce. Using comprehensive datasets of

market-oriented smallholder farm households from Indonesia and Kenya, and subsistence farmers

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.

Recent research has analyzed whether higher levels of farm production diversity contribute to improved diets in smallholder farm households. We add to this literature by using and comparing different indicators, thus helping to better understand some of the underlying linkages. The analysis builds on data from Indonesia, Kenya, and Uganda. On the consumption side, we used 7-day food recall data to calculate various dietary indicators, such as dietary diversity scores, consumed quantities of fruits and vegetables, calories and micronutrients, and measures of nutritional adequacy.