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Growing populations and changing diets associated with greater wealth increase the pressure on the world's available land, constituting serious threats to food security. Policies to reverse this situation have aimed mainly at increasing agricultural yields and productivity, but these efforts are often cost- and time-intensive. Agri-food systems must be transformed to provide enough quantity of healthy food for everyone in a sustainable way, including those involved in the production chain, while dealing with the dynamics of local and global economies and the environment. Transforming the agri-food systems requires a combination of research, policies, and investments to manage complex trade-offs.Food loss and food waste (FLW) is one essential element of the agri-food system transformation, which touches not only the productivity efficiency of using natural resources but also the reduction of Greenhouse gas emissions. FLW, as a result, has become an increasingly important topic in the development community. Food losses represent 14% of global production (FAO, 2019). This is equivalent to US$400 billion annually. In fact, the United Nations included the issue of food loss and waste in the Sustainable Development Goal target 12.3, which aims to "halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains, including post-harvest losses" by 2030. Greenhouse gas emissions linked with food losses are equivalent to about 1.5 gigatonnes of CO2. In addition, food loss entails excessive use of scarce resources. For example, each year, 75 billion cubic meters of water are used to produce fruits and vegetables that are not eaten. Finally, the loss of marketable food can reduce producers' income and increase consumers' expenses, likely having larger impacts on disadvantaged population segments. The losses of fruit and vegetables are equivalent to 912 trillion kilocalories and micronutrients. This is happening, as 3

Crop diseases imply a high cost to control them and losses in the yield. Besides, effectiveness of conventional control methods such as pesticides can decrease in their effectiveness. There is a greater awareness of the fact that the optimal crop disease management system requires a combination of different approaches. Soil management may be a component that would modulate the expression of crop diseases. Despite of reports on the influence of soil conditions in the control of diseases, results are inconsistent. Based on the literature, a conceptual framework is proposed, where three types of soil influence in disease incidence are identified: a direct influence, where the pathogen is affected; an indirect influence, where the plant’s response to the disease is modulated, and a third, where both the first and the second occur simultaneously (Chapter 1). The model was examined for the case of Fusarium wilt (Fusarium oxysporum f. sp. cubense or Foc) in banana (Musa sp.). Tropical Race 4 (Foc TR4), one strain of this soil borne fungus could sweep away 80% of the whole bananas. It represents a high risk for the large-scale banana production and also for small-system. Foc Race 1 devastated the Gros Michel cultivar in Latin America and the Caribbean (LAC) during the 20th century. Social and economic impacts were truly significant. The was the shift of the Gros Michel to cultivars from de largely resistant to Foc Race 1 sub-group Cavendish (Musa AAA). With the reports of the new strain Foc TR4 and its wide range of susceptible cultivars, the research in the disease was reactivated. Foc can survive in the soil for decades. To better understand the Foc-soil-banana relationship, the research was divided in five phases in where different research tool with advantages and disadvantages were combined: literature, greenhouse experiments, field experiments and farm survey. The research questions were: i) Which soil properties are known to have an important role in disease expressi

Agriculture is the largest contributor of non-CO2 anthropogenic greenhouse gas emissions (GHG). Agricultural based mitigation strategies (e.g. no-tillage) are identified to reduce emissions from agricultural soils through improved agricultural management. Global ecosystem models that are usually used for finding the potential of agricultural based mitigation strategies are limited, because processes related to agricultural management are currently underrepresented in global ecosystem models. The aim of this thesis is to contribute to the representation of agricultural management in global ecosystem models, so that the potential of agricultural based mitigation practices can be better quantified. Therefore, this thesis first addressed how processes related to agricultural management can be described in global ecosystem models, with a focus on processes related to tillage and N2O emissions. This analysis resulted in a standardized framework that can be followed to incorporate other agricultural management practices in global ecosystems as well. After indicating how processes related to tillage can be described, they were implemented into the global ecosystem model LPJmL. Subsequently, the extended LPJmL model was evaluated on its performance on various fluxes (including N2O and CO2) at the global scale and for a number of experimental sites. Finally, the uncertainty caused by the upscaling of soil input data when assessing tillage effects on N2O emissions were addressed. LPJmL was not capable of accurately simulating tillage effects on N2O emissions. Hence, the potential of mitigating N2O emissions through tillage management cannot be well assessed. However, the implementation of the more detailed tillage-related mechanism into the global ecosystem model LPJmL improved the ability to understand agricultural management options for agricultural mitigation of CO2 emissions, climate change adaptation and reducing environmental impacts. The work in this thesis concludes th

With limitations in the availability and accessibility of surface water, attention is increasingly directed towards groundwater resources as the most reliable source of fresh water for different sectors. Accordingly, groundwater is over abstracted in many countries of the world. The overexploitation of groundwater renewable and non-renewable aquifers for both urban use and irrigation results in a drop of the water table and, frequently, in a reduction in groundwater quality. This study focuses on the use of groundwater for irrigation purposes in desert areas of the MENA region. The thesis contributes to conserving and sustaining the use of limited groundwater resources in desert agriculture, by analyzing the current unsustainable use of groundwater, focusing on groundwater policy on the one hand, and on the specificities of desert agriculture on the other, with a focus on Azraq basin in Jordan.

The United Nations pledged to achieve the Sustainable Development Goals by 2030. Regional land use analyses (RLUA) have an essential contribution to achieving these goals. To better meet the needs for achieving sustainable development, RLUA became more quantitative and more interdisciplinary over recent decades. This change resulted in an increased use of quantitative simulation models, which changed the type and nature of input data as well. Soil data are one of the input data RLUA require. Available soil data often do not meet the soil data requirements anymore, due to the change in RLUA. Therefore, a gap exists between the available and required soil data. This thesis aims to find possible solutions to bridge this gap. In Chapter 2, different soil datasets are compared to identify the gap and to analyse the effect of using different soil datasets as input for a regional land use analysis (RLUA). Main challenges with soil data in RLUA are: i) understanding the assumptions in soil datasets, ii) creating soil datasets that meet the requirements for regional land use analysis, iii) not only rely on available soil data but also collect new soil data and iv) validate soil datasets. Chapter 2 demonstrated differences between soil datasets, which had significant effect on the results of RLUA. Three potential solutions on bridging the gap between the available and required soil data are given in Chapter 3, 4 and 5. A literature study showed that RLUA hardly combine available and newly collected soil data. Chapter 3 analyses what complementary data RLUA require by combining available soil data and newly collected soil data. Two case studies were carried out to illustrate how a combination can enrich the soil data for RLUA. Predicting soil properties, in particular soil organic matter, using newly collected soil data often result in soil maps of poor quality. The digital soil mapping techniques that are currently being used for predicting soil properties make dominantly use