Household level modelling in CCAFS and other CRPs: Moving from ideas to action

Across different CGIAR centres there is a strong interest in applying farm household models to evaluate and target adaptation to climate change and variability. The Commonwealth Scientific and Industrial Research Organization (CSIRO), an important research partner for the CGIAR Research Program (CRP) on Climate Change, Agriculture and Food Security (CCAFS), has developed into a top institute in this research area, and has strong interest in strengthening collaboration with CCAFS and other CRPs like Integrated Systems for the Humid Tropics. In recent years CCAFS and Humid Tropics have invested a lot of time and effort in collecting farm household level characterization data and in developing coherent socio-economic scenarios of change in the near future. Given the current interest in household level analyses of adaptation and mitigation options, and the availability of data and resources within CCAFS and other CRPs the aim of the workshop was to develop a community of practice across CGIAR centres and stimulate active CGIAR – CSIRO collaboration, thereby more effectively sharing and further developing the wide range of tools and approaches available in the different institutes. The workshop, besides exchanging information about new results of on-going work and sharing of approaches and methods also produced a flexible and stepwise work plan that could be implemented under different scenarios of available funding

Optical instruments for measuring leaf area index in low vegetation : application in Arctic ecosystems

Author Posting. © Ecological Society of America, 2005. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 15 (2005): 1462–1470, doi:10.1890/03-5354.Leaf area index (LAI) is a powerful diagnostic of plant productivity. Despite the fact that many methods have been developed to quantify LAI, both directly and indirectly, leaf area index remains difficult to quantify accurately, owing to large spatial and temporal variability. The gap-fraction technique is widely used to estimate the LAI indirectly. However, for low-stature vegetation, the gap-fraction sensor either cannot get totally underneath the plant canopy, thereby missing part of the leaf area present, or is too close to the individual leaves of the canopy, which leads to a large distortion of the LAI estimate. We set out to develop a methodology for easy and accurate nondestructive assessment of the variability of LAI in low-stature vegetation. We developed and tested the methodology in an arctic landscape close to Abisko, Sweden. The LAI of arctic vegetation could be estimated accurately and rapidly by combining field measurements of canopy reflectance (NDVI) and light penetration through the canopy (gap-fraction analysis using a LI-COR LAI-2000). By combining the two methodologies, the limitations of each could be circumvented, and a significantly increased accuracy of the LAI estimates was obtained. The combination of an NDVI sensor for sparser vegetation and a LAI-2000 for denser vegetation could explain 81% of the variance of LAI measured by destructive harvest. We used the method to quantify the spatial variability and the associated uncertainty of leaf area index in a small catchment area.This research was funded by U.S. National Science Foundation grant DEB0087046

How can the Data Revolution contribute to climate action in smallholder agriculture?

In this article, we discuss the ongoing Data Revolution in relation to climate action in agriculture. Data are highly relevant for climate action, as climate change makes current local knowledge increasingly irrelevant and requires smarter management of agricultural systems. We discuss five datarelated concepts and explore how they are linked with agricultural climate action: lean data, crowdsourcing, big data, ubiquitous computing, and information design. We show practical examples for each of these concepts. There are many opportunities for improving agricultural development projects, providing new services to smallholder farmers, and generating better information for policy- and decision-making. Making the Data Revolution work for smallholder farmers’ climate action not only takes further technological development, but also requires careful governance and public investment to avoid a few actors taking over the current innovation space and stifle further development

Tight coupling between leaf area index and foliage N content in arctic plant communities

Author Posting. © The Authors, 2004. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Oecologia 142 (2005): 421-427, doi:10.1007/s00442-004-1733-x.The large spatial heterogeneity of arctic landscapes complicates efforts to quantify key processes of these ecosystems, for example productivity, at the landscape level. Robust relationships that help to simplify and explain observed patterns, are thus powerful tools for understanding and predicting vegetation distribution and dynamics. Here we present the same linear relationship between leaf area index and total foliar nitrogen, the two factors determining the photosynthetic capacity of vegetation, across a wide range of tundra vegetation types in both Northern-Sweden and Alaska between leaf area indices of 0 and 1 m2 m-2, which is essentially the entire range of leaf area index values for the Arctic as a whole. Surprisingly, this simple relationship arises as an emergent property at the plant community level, whereas at the species level a large variability in leaf traits exists. As the relationship between LAI and foliar N exists among such varied ecosystems, the arctic environment must impose tight constraints on vegetation canopy development. This relationship simplifies the quantification of vegetation productivity of arctic vegetation types as the two most important drivers of productivity can now be estimated reliably from remotely sensed NDVI images.This work was funded by the US National Science Foundation

Workshop report: Farm-household modelling with a focus on food security, climate change adaptation, risk management and mitigation: a way forward

Related working paper at http://hdl.handle.net/10568/21112The workshop entitled: ‘Farm-household modelling with a focus on food security, climate change adaptation, risk management and mitigation: a way forward’ focused on identifying the current strengths and weaknesses of farm and household-level models, and laying out practical pathways to improve these models. This activity followed a recent review on farm household modelling commissioned by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). The workshop took place in Amsterdam, The Netherlands on 23–25 April 2012. The most important conclusions of the workshop were: 1. It is possible to analyse household-level questions related to climate change in a reasonable short (6 months to 1 year) time span with existing tools and the expertise present in the group of participants. 2. Availability of component tools can be an issue; the tools are there but free usability of code and parameters is not always possible. 3. Activities to develop repositories of models and data are urgently needed to increase further development of household models and make better use of existing knowledge. A set of activities will be developed to move the work forward in three CCAFS target regions (West Africa, East Africa and South Asia). The expectation is that the workshop will serve as a springboard for a multi-year initiative that will eventually involve a wide range of participants both within and outside the CGIAR. The challenges associated with climate change, agriculture and food security are considerable, and household modelling has a key role to play in designing and evaluating adaptation, risk management and mitigation options that can help lead to the positive outcomes that CCAFS and research-for-development partners are seeking

Разработка распределенной системы концептуального проектирования сложных технических объектов

Small wetlands increasingly become important agricultural production niches in sub-Saharan Africa. Understanding the diversity of these households may help to develop guidelines for their future use. In this study a typology of households in small wetlands was developed using case studies of 275 farmers from Kenya and Tanzania. Based on a combination of production system attributes land resources, and production objectives, households were categorised into 12 farm types. Production resources, access to cropland on upland, access to market, and non-wetland related livelihood strategies differed between households and translated into different wetland use patterns. Farm types were linked to the prevailing wetland systems. The household typology captured various dimensions in values, attitudes, and goals of farmers and determined their influence on land use decisions. The wetland field: farm size ratio differed significantly between farm types. More than one-third of the households depended completely on cropland in the wetland. The variable nature of household dependence was reflected in diverse production orientations with different levels of land use intensity and subsequent pressure on wetlands. The heterogeneity induced agricultural practices among households and the complexity of the wetland system highlight the need for specific incentives to develop sustainable plans for wetland management