This chapter summarises the key insights from the case studies included in the book. First, it discusses the technology-deforestation link in six different types of cases: developed countries, commodity booms, shifting cultivation, permanent upland (rainfed) agriculture, irrigated (lowland) agriculture, and cattle production. Next, it returns to the hypotheses presented in the book, and discusses the key conditioning factors in the technology-deforestation link. A number of factors determine the outcome.

The Amazon Basin is so diverse that one could say many Amazons exist, not just one. Indeed, its diversity is considered unique in the world. Although the Basin occupies 7% of the planet’s land, it carries 25% of the world’s terrestrial biodiversity. The region is so vast, it represents one-third of South America’s land surface. It covers, or partly covers, nine countries: Colombia (36% of the land area), Venezuela and Guyana (6% each), Suriname and French Guiana (almost 100% each), Brazil (60%), Bolivia and Peru (75% together), and Ecuador (45%).

Amazonia’s abundant natural resources underpin water, energy, food and health security for the people and economies of the region and far beyond. At the heart of this nexus of securities is water. So abundant in the region, but now under increasing threat as industrial and agricultural pollution increases, and extreme droughts reveal a once unthinkable water vulnerability.

Over the past several decades, DRP Korea has suffered from extensive land degradation leading to the loss of livelihoods causing increased food insecurity. To restore degraded sloping land, the Ministry of Land and Environmental Protection of DPR of Korea (MoLEP), the Swiss Agency for Development and Cooperation (SDC) and the World Agroforestry Centre (ICRAF), have been promoting the multi-purpose use of trees to transform landscapes and livelihoods using a participatory approach.

Rangeland scientists and quantitative ecologists have developed numerous methods and monitoring techniques that can be used for vegetation sampling (Barbour et al. 1987). The methods used to position samples (transects, quadrats, lines, and points) vary and can be classed as selective, capricious, systematic, or random. One of the prerequisites for valid statistical inference is that samples are taken randomly. A random sampling procedure implies that all elements or units of the population being studied have an equal chance of being represented in the sample.

The contemporary concern about anthropogenic release of greenhouse gas (GHG) into the

environment and the contribution of livestock to this phenomenon have sparked animal

scientists’ interest in predicting methane (CH4) emissions by ruminants. Focusing on milk

production, we address six basic nutrition models or feeding standards (mostly empirical

systems) and five complex nutrition models (mostly mechanistic systems), describe their key

characteristics, and highlight their similarities and differences. Four models were selected to

This training resource is presented in five modules. 1) Global perspectives on animal genetic resources for sustainable agriculture and food production in the tropics. This module provides some insight into the need for better use of animal genetic resources (AnGR) in the context of projected demand for food in developing countries until 2020. 2) Improving our knowledge of tropical indigenous animal genetic resources.