Data requirements for crop modelling-Applying the learning curve approach to the simulation of winter wheat flowering time under climate change

A prerequisite for application of crop models is a careful parameterization based on observational data. However, there are limited studies investigating the link between quality and quantity of observed data and its suitability for model parameterization. Here, we explore the interactions between number of measurements, noise and model predictive skills to simulate the impact of 2050′s climate change (RCP8.5) on winter wheat flowering time. The learning curve of two winter wheat phenology models is analysed under different assumptions about the size of the calibration dataset, the measurement error and the accuracy of the model structure. Our assessment confirms that prediction skills improve asymptotically with the size of the calibration dataset, as with statistical models. Results suggest that less precise but larger training datasets can improve the predictive abilities of models. However, the non-linear relationship between number of measurements, measurement error, and prediction skills limit the compensation between data quality and quantity. We find that the model performance does not improve significantly with a theoretical minimum size of 7–9 observations when the model structure is approximate. While simulation of crop phenology is critical to crop model simulation, more studies are needed to explore data needs for assessing entire crop models

Methods for environment: productivity trade-off analysis in agricultural systems

Trade-off analysis has become an increasingly important approach for evaluating system level outcomes of agricultural production and for prioritising and targeting management interventions in multi-functional agricultural landscapes. We review the strengths and weakness of different techniques available for performing trade-off analysis. These techniques, including mathematical programming and participatory approaches, have developed substantially in recent years aided by mathematical advancement, increased computing power, and emerging insights into systems behaviour. The strengths and weaknesses of the different approaches are identified and discussed, and we make suggestions for a tiered approach for situations with different data availability. This chapter is a modified and extended version of Klapwijk et al. (2014)

An integrated agro-ecosystem and livelihood systems approach for the poor and vulnerable in dry areas

More than 400 million people in the developing world depend on dryland agriculture for their livelihoods. Dryland agriculture involves a complex combination of productive components: staple crops, vegetables, livestock, trees and fish interacting principally with rangeland, cultivated areas and watercourses. Managing risk and enhancing productivity through diversification and sustainable intensification is critical to securing and improving rural livelihoods. The main biophysical constraints are natural resource limitations and degradation, particularly water scarcity and encroaching desertification. Social and economic limitations, such as poor access to markets and inputs, weak governance and lack of information about alternative production technologies also limit the options available to farmers. Past efforts to address these constraints by focusing on individual components have either not been successful or are now facing a declining rate of impact, indicating the need for new integrated approaches to research for development of dryland systems. This article outlines the characteristics of such an approach, integrating agro-ecosystem and livelihoods approaches and presents a range of empirical examples of its application in dryland contexts. The authors draw attention to new insights about the design of research required to accelerate impact by integrating across disciplines and scales

A review of food security and the potentials to develop spatially informed food policies in Bangladesh

Background:Food security globally depends primarily on three components: food availability, food access, and food utilization. Regional variations of these components may affect food security via spatial differences in natural, social or economic conditions and the interaction of these in a complex environmental system.Purpose:It is important to understand the regional variation of food security, particularly where and under what natural and socio-economic circumstances people become vulnerable to low food security in a country.Methods:This article provides an overview of food security in Bangladesh in terms of the three main components, identifies knowledge gaps in present food security research, reviews possible impacts of climate change on food security, and sourced a wide range of spatio-temporal data relevant for food security.Results:The study highlights potentials and indicates different processes to develop spatially informed food policies in a country, particularly focuses on Bangladesh. This will contribute to improved food security by considering regional food security conditions, region-specific deficits, climate change, other future risks, and devises actions related to the respective components.Conclusion:The study concludes that different processes can provide a foundation for policy development and these will advance research-policy linkage to improved food security