Allen, W. NRM-Changelinks.Net. Links for developing Change in Natural Resource Management: an on-line resource guide for those seeking to improve the use of collaborative and learning-based approaches. Manaaki Whenua - Landcare Research, New Zealand and Natural Resource Management Programme, Massey University, New Zealand.

Candelo, C. Cárdenas, J.C, JE. Correa, M.C. López, D.L. Maya y M. X. Zorrilla and A.M.Roldan. 2002. Juegos económicos y diagnostico rural participativo. Un manual con ejemplos de aplicación para la cooperación. Universidad Javeriana y WWF Colombia.

Loevinsohn, M. (Ed.) 2002. Deepening the Basis of Rural Resource Management. Agricultural Systems 73(1) Special Issue.This Special Issue of Elsevier Science's Agricultural Systems includes eight of the papers presented at a workshop entitled “Deepening the Basis of Rural Resource Management,” held at ISNAR in The Hague in February 2000. The workshop brought together researchers working in diverse situations and with resources of different types -- natural, human, and economic -- who are developing innovative methods aimed at enabling farming communities to adjust their decision making in the face of rapid and significant change. The workshop sought to throw light on four main questions: 1. What are the features of methods that are effective in supporting farmers’ decision making where resource systems are undergoing such change? 2. How do the features of effective methods vary in different types of resource management situations? 3. What approaches are available to assess the impact of these methods? 4. What institutional factors have favored or hindered the development of effective decision support methods and their use over wider areas? The articles in this Special Issue include a critical review of the key issues emerging from the workshop, five diverse case studies and one of two theme presentations, on the state of the art in decision support in rural resource management. The other theme paper, on learning theory and its relevance for rural resource management, can be found in the workshop’s proceedings, as can the other nine case studies.

Beaulieu, N., G. Leclerc, M. Alvarez, G. De Wispeleare, J. Jaramillo, Y. Rubiano, A. Fajardo, O. Muñoz, N. Peñuela. 2001. A Proposed Framework for Using Remote Sensing Imagery to monitor environmental dynamics in support to local planning efforts. Presented at the Workshop on Integrated Natural Resources Management (INRM), 28-31 August, 2001, CIAT, Cali, Colombia.

Carberry, P.S., Z. Hochman, R.L. McCown et al. 2002. The FARMSCAPE approach to decision support: farmers', advisers', researchers' monitoring, simulation, communication and performance evaluation. Agricultural Systems, 74:141-177.FARMSCAPE (Farmers', Advisers', Researchers', Monitoring, Simulation, Communication And Performance Evaluation) is a program of participatory research with the farming community of northeast Australia. It initially involved research to explore whether farmers and their advisers could gain benefit from tools such as soil characterisation and sampling, climate forecasts and, in particular, simulation modelling. Its current focus is facilitating the implementation of commercial delivery systems for these same tools in order to meet industry demand for their access. This paper presents the story of what was done over the past decade, it provides performance indicators of impact, it reflects on what was learnt over this period and it outlines where this research is likely to head in the future.Over the past 10 years, the FARMSCAPE team employed a Participatory Action Research approach to explore whether farmers could value simulation as a decision support tool for managing their farming system and if so, could it be delivered cost-effectively. Through farmer group engagement, on-farm trials, soil characterisation, monitoring of crops, soils and climate, and sessions to apply the APSIM systems simulator, FARMSCAPE represented a research program on decision support intervention. Initial scepticism by farmers and commercial consultants about the value of APSIM was addressed by testing its performance both against measured data from on-farm trials and against farmers' experiences with past commercial crops. Once this credibility check was passed, simulation sessions usually evolved into participants interactively inquiring of the model the consequence of alternative management options. These `What if' questions using APSIM were contextualised using local climate and soil data and the farmer's actual or proposed management rules.The active participation of farmers and their advisers, and working in the context of their own farming operations, were the key ingredients in the design, implementation and interpretation of the FARMSCAPE approach to decision support. The attraction of the APSIM systems simulator to farmers contemplating change was that it allowed them to explore their own system in a manner equivalent to learning from experience. To achieve this, APSIM had to be credible and flexible. While direct engagement of farmers initially enabled only a limited number of beneficiaries, this approach generated a commercial market for timely and high quality interactions based on soil monitoring and simulation amongst a significant sector of the farming community. Current efforts are therefore focused on the training, support and accreditation of commercial agronomists in the application of the FARMSCAPE approach and tools.

Heong, K.L. and Escalada, M.M. 1999. Quantifying rice farmers' pest management decisions - Beliefs and subjective norms in stem borer control. Crop Protection, 18:315-322. Request reprintThe paper introduces the pest belief model and Fishbein and Ajzen's theory of reasoned action to analyze farmers' decisions in stem borer management. Farmers spent an average of $39/ha (median $18) on insecticides believing that if they had not controlled an average loss of 1004 kg/ha or $402 (median 592, $237) would occur. Farmers' estimates of the worst attack averaged 19 white heads/m2 (median10) with the associated average loss of 1038 kg/ha or $415 (median 592, $270), implying that farmers' decisions were guided by the worst attacks. Perceived benefits from insecticides were directly related with farmers' insecticide use and perceived severity. Perceived susceptibility was also high, with 59% of farmers believing that a loss of 450 kg/ha would be "extremely or very likely". Farmers believed insecticides could destroy natural enemies but placed only moderate importance to conserving them. Health was believed to be very important but farmers had mixed beliefs that spraying could bring about poor health. This study also provides evidence suggesting high peer pressure on farmers' spray decisions directly influencing perceived benefits from sprays, insecticide spending and spray frequency.

McCown, R.L., Z. Hochman and P. S. Carberry. 2002. Probing the enigma of the decision support system for farmers: Learning from experience and from theory. Agricultural Systems. 74:1-10.Although not conspicuous in its literature, agricultural modelling and its applications have inherited much from the field of operational (operations) research. In the late 1940s, techniques for mathematically simulating processes came into agricultural science directly from industry. The decision support system (DSS) concept followed almost 40 years later. It seems that the large differences between farm production and its management and industrial production and its management account for the failure of agricultural systems scientists to be more attentive students of the experiences in this parent field. In hindsight, the penalty of this is greatest in the matter of the problematic socio-technical relationship between scientific models built to guide practice and actual practice. As a socio-technical innovation, the agricultural DSS has much more in common with DSSs in business and industry than might be expected judging by the domain knowledge content. One implication is that the crisis in the parent field concerning the `problem of implementation' could have served as a cautionary tale for agriculture. Although this opportunity was missed, it is not too late to tap problem-structuring and problem-solving insights from operations research/management science to aid our thinking about our own `problem of implementation'. This paper attempts this in constructing a framework for thinking about subsequent papers in this Special Issue.

Escalada, M.M. and K.L. Heong. Participatory Frameworks for Facilitating Interdisciplinary Research in Rice Pest Management. Synopsis of forthcoming book.

Gachengo, C., C.A. Palm, E. Adams, K.E. Giller, R.J. Delve, and G. Cadisch. 1998. Organic Resource Database. TSBF 1998 Annual Report. TSBF, Nairobi.The purpose of the Organic Resource Database (ORD) is to:- collate existing data on plant quality characteristics such as chemical and physical attributes, decomposition behavior in soils and animal feed value- allow users to compare their data with comparable literature data- provide input data for soil decomposition and nutrient cycling models- provide guidelines for a minimal dataset to characterize plant quality- provide decision tools to identify best use options for these organic materials as soil amendments- identify relationships between plant quality parameters and environmental conditions- allow the user to add new dataThe main species covered are tropical legumes. Plant materials are separated into the different plant components, (e.g. leaves, stems, whole shoots, roots) accounting for much of the variation found among data for the same species. To provide a fair basis of comparison between different datasets methods for some critical quality parameters, like polyphenols, are given.The database is aimed at researchers, extensionists, NGOs and ultimately farmers.

Barrios, E., M. Bekunda, R. Delve, A. Esilaba and J. Mowo. 2000. Methodologies for Decision Making in Natural Resource Management: Identifying and Classifying Local Indicators of Soil Quality. Eastern Africa Version. CIAT, SWNM, TSBF, AHI. ISBN: 958-694-013-6.The increasing interest in local soil knowledge is largely due to the realization that farmer communities that have been interacting with their soils for a long time can provide many insights into the sustainable management of tropical soils. A participatory approach, in the form of a methodological guide, has been developed and used in Latin America and Africa to identify and classify local indicators of soil quality related to permanent and modifiable soil properties. This methodological tool aims to empower local communities to better manage their soil resources through improved decision making and monitoring of their environment. It is also designed to steer soil management towards developing practical solutions to identified soil constraints and monitoring the impact of the management strategies implemented to address such constraints. The methodological approach presented here constitutes one tool to capture local demands and perceptions of soil constraints as an essential guide to relevant research and development activities. A significant component of this approach is the collaboration between technical officers and farmers to build an effective communication channel with each other. The participatory process also places considerable emphasis on consensus building among farmers to determine those soil-related constraints that should be tackled first. Such consensus is an important step toward collective action by farming communities if improved soil management strategies are to be adopted at a landscape scale.

Hern√°ndez, L.A. 1999. Logistic Regression for Analysis of Preference. An application for Excel. v. 7. CD-Rom.CIAT, Cali, Colombia. ISBN:958-694-027-6"Preference Ranking" is a tool that explains decisions of acceptance or rejection by identifying the criteria used to select one technology option over another. The preference ranking analysis is based on logistic regression. It facilitates data management and makes it possible to simulate the criteria for acceptance of a technology. The software consists of a matrix in Excel 7.0 for Windows. The user only needs to input field data into an existing file containing the frequency of each technology and its ranking order. When the user has finished this process, the software conducts the analysis automatically. For each technology option, the application produces a ranking order, frequency, probability, and cumulative probability. A graph is generated for each technology option which shows the cumulative probability versus ranking order, and statistical differences (slope m, standard deviation SE, and coefficient of correlation r2). A higher slope with a positive intercept indicates a greater degree of acceptance within the highest ranked options. A higher slope but with a negative intercept indicates rejection, and probability of acceptance for the lowest ranking options. Comparison of acceptance levels across technologies and statistics are also shown. Manual with diskette. The manual contains 22 p. 28 x 21.5 cm. Also available in Spanish.

Barrios, E, and M.T. Trejo 2003. Implications of local soil knowledge for integrated soil management in Latin America. Geoderma 111 (2003) 217–231.The increasing attention paid to local soil knowledge in recent years is the result of a greater recognition that the knowledge of people who have been interacting with their soils for long time can offer many insights about the sustainable management of tropical soils. This paper describes two approaches in the process of eliciting local information. Case studies show that there is a consistent rational basis to the use of local indicators of soil quality and their relation to improved soil management. The participatory process used is shown to have considerable potential in facilitating farmer consensus about which soil-related constraints should be tackled first. Consensus building is presented as an important step prior to collective action by farming communities in integrated soil management at the landscape scale. Taking advantage of the complementary nature of local and scientific knowledge is highlighted as an overall strategy for sustainable soil management.

Morin, S., F. Palis, K. McAllister, A. Papag, and M. Magsumbol. 2001. Farmer Learning and the International Research Centres: Lessons from IRRI. IIED Gatekeeper Series Issue 96.The International Rice Research Institute (IRRI) is one of 16 centres in the Consultative Group for International Agricultural Research (CGIAR). IRRI has a huge mandate: to conduct research and training to improve the lives of rice producers and consumers, particularly those with low incomes. This broad mandate means that whilst IRRI generates knowledge and products such as improved pest management methods or new varieties and machinery, it is the role of extension workers from other organisations to promote and disseminate these to the farmers. This means that the dissemination of the research outputs is outside IRRI’s control. However, some IRRI researchers have recently developed ‘decision aids’ as a way for farmers to adopt and adapt technologies on a much wider scale than can be achieved through focused research projects alone. In this paper we highlight one of these innovative approaches, the development and promotion of the ‘no early spray’ (NES) technique in integrated pest management in Central Luzon, Philippines, and discuss its implications for farmer learning within the institutional culture of IRRI. The NES technique grew from research revealing that farmers’ belief that leaf folders caused yield-reducing damage in rice was incorrect. The research showed that in fact spraying for leaf folder is not necessary up to 40 days after transplanting. This simple message was tested by farmers in the Philippines, prompting them to conduct site specific research and to entirely cease spraying their fields for leaf folder when they found it to be true. These experiences suggest that decision aids work best when the decisions farmers are encouraged to take are not mandated or fixed, but prompted by the research process itself. The NES started out as a decision aid and, through farmer experimentation, turned into a learning tool. The success of NES indicates that decision aids offer ways to have impact and foster farmer learning at the farm level within the corporate culture of IRRI, and other similar International Agricultural Research Centres.

Horne, P.M. and W.W. Stur. 1999. Developing forage technologieswith smallholder farmers: How to select the best varieties to offer farmers in Southeast Asia. ACIAR Monograph No. 62. ACIAR/CIAT.Livestock are an important component of upland farming systems in Southeast Asia. In the past, feed resources were plentiful. On many farms this is no longer the case, so farmers have to spend more and more time finding feed for their animals. Planting forages can help to overcome this problem. However, no two farms have the same resources and needs. Forages that are suitable on one farm may not be suitable for other farms. The best way to develop the 'right' forage technologies for each farm is for farmers to evaluate promising forage technologies and adapt the best options to their situation.In this participatory approach the role of the development worker is to give farmers information about forages that may solve their problems. There are many forages and ways of growing them on farms. Not all will be suitable for a particular situation and need. This booklet will help development workers to select appropriate forage options to offer farmers.

Harrington, L. J. White, P. Grace, D. Hodson, A.D. Hartkamp, C. Vaughan and C. Meisner. 2002. Delivering the Goods: Scaling out Results of Natural Resource Management Research. Special Feature on Integrated Natural Resource Management (INRM). Conservation Ecology 5(2).To help integrated natural resource management (INRM) research "deliver the goods" for many of the world's poor over a large area and in a timely manner, the authors suggest a problem-solving approach that facilitates the scaling out of relevant agricultural practices. They propose seven ways to foster scaling out: (1) develop more attractive practices and technologies through participatory research (2) balance supply-driven approaches with resource user demands, (3) use feedback to redefine the research agenda, (4) encourage support groups and networks for information sharing, (5) facilitate negotiation among stakeholders, (6) inform policy change and institutional development, and (7) make sensible use of information management tools, including models and geographic information systems (GIS). They also draw on experiences in Mesoamerica, South Asia, and southern Africa to describe useful information management tools, including site similarity analyses, the linking of simulation models with GIS, and the use of farmer and land type categories.

Guijt, I., Berdegue, J.A. and Loevinsohn, M. 2000. Deepening the Basis of Rural Resource Management. Proceedings of a workshop, February 16-18 2000, The Hague. ISNAR and RIMISP (Red Internacional de Metodologia de Investigaciones de Sistemas de Produccion,. 222pp. Also published in 2002 as a  Special Issue of Agricultural Systems:The workshop brought together researchers working in diverse situations and with resources of different types -- natural, human, and economic -- who are developing innovative methods aimed at enabling farming communities to adjust their decision making in the face of rapid and significant change. The workshop sought to throw light on four main questions:1. What are the features of methods that are effective in supporting farmers’ decision making where resource systems are undergoing such change?2. How do the features of effective methods vary in different types of resource management situations?3. What approaches are available to assess the impact of these methods?4. What institutional factors have favored or hindered the development of effective decision support methods and their use over wider areas?The articles in this Special Issue include a critical review of the key issues emerging from the workshop, five diverse case studies and one of two theme presentations, on the state of the art in decision support in rural resource management. The other theme paper, on learning theory and its relevance for rural resource management, can be found in the workshop’s proceedings, as can the other nine case studies.

Carberry, P. and A. Whitbread. are members of the APSRU team that developed the Agricultural Production Systems Simulator (APSIM) model and FARMSCAPE an action research project that set out to understand and change the use of Decision Support Systems in farmers’ management practice in Australia.