Farming systems methodology for efficient resource management at the farm level: a review from an Indian perspective

Farming systems research is a multi-disciplinary holistic approach to solve the problems of small farms. Small and marginal farmers are the core of the Indian rural economy Constituting 0.80 of the total farming community but possessing only 0.36 of the total operational land. The declining trend of per capita land availability poses a serious challenge to the sustainability and profitability of farming. Under such conditions, it is appropriate to integrate land-based enterprises such as dairy, fishery, poultry, duckery, apiary, field and horticultural cropping within the farm, with the objective of generating adequate income and employment for these small and marginal farmers Under a set of farm constraints and varying levels of resource availability and Opportunity. The integration of different farm enterprises can be achieved with the help of a linear programming model. For the current review, integrated farming systems models were developed, by Way Of illustration, for the marginal, small, medium and large farms of eastern India using linear programming. Risk analyses were carried out for different levels of income and enterprise combinations. The fishery enterprise was shown to be less risk-prone whereas the crop enterprise involved greater risk. In general, the degree of risk increased with the increasing level of income. With increase in farm income and risk level, the resource use efficiency increased. Medium and large farms proved to be more profitable than small and marginal farms with higher level of resource use efficiency and return per Indian rupee (Rs) invested. Among the different enterprises of integrated farming systems, a chain of interaction and resource flow was observed. In order to make fanning profitable and improve resource use efficiency at the farm level, the synergy among interacting components of farming systems should be exploited. In the process of technology generation, transfer and other developmental efforts at the farm level (contrary to the discipline and commodity-based approaches which have a tendency to be piecemeal and in isolation), it is desirable to place a whole-farm scenario before the farmers to enhance their farm income, thereby motivating them towards more efficient and sustainable fanning.

Small towns as 'sub-poles' in English rural development: Investigating rural-urban linkages using sub-regional social accounting matrices

This paper uses spatial economic data from four small English towns to measure the strength of economic integration between town and hinterland and to estimate the magnitude of town-hinterland spill-over effects. Following estimation of local integration indicators and inter-locale flows, sub-regional social accounting matrices (SAMs) are developed to estimate the strength of local employment and output multipliers for various economic sectors. The potential value of a town as a 'sub-pole' in local economic development is shown to be dependent on structural differences in the local economy, such as the particular mix of firms within towns. Although the multipliers are generally small, indicating a low level of local linkages, some sectors, particularly financial services and banking, show consistently higher multipliers for both output and employment. (c) 2007 Elsevier Ltd. All rights reserved.

Biodiversity of Collembola in urban soils and the use of Folsomia candida to assess soil 'quality'

The effects of metal contamination on natural populations of Collembola in soils from five sites in the Wolverhampton area ( West Midlands, England) were examined. Analysis revealed that metal concentrations were elevated above background levels at all sites. One location in particular (Ladymoor, a former smelting site) was highly contaminated with Cd, Cu, Pb and Zn at more than 20 times background levels. Biodiversity indices ( Shannon - Weiner, Simpson index, Margalef index, alpha index, species richness, Shaneven ( evenness) and Berger - Parker dominance) were calculated. Of these indices, estimates of species richness and evenness were most effective at highlighting the differences between the Collembola communities. Indeed, the highest number of species were found at the most contaminated site, although the Collembola population also had a comparatively low evenness value, with just two species dominating. The number of individuals per species were allocated into geometric classes and plotted against the cumulative number of species as a percentage. At Ladymoor, there were more geometric classes, and the slope of the line was shallower than at the other four sites. This characteristic is a feature of polluted sites, where a few species are dominant and most species are rare. The Ladymoor soil also had a dominance of Isotomurus palustris, and was the only site in which Ceratophysella denticulata was found. Previous studies have shown that these two species are often found in sites subject to high metal contamination. Survival and reproduction of the "standard'' test springtail, Folsomia candida (Willem), were determined in a 4 week exposure test to soils from all five sites. Mortality was significantly increased in adults and reproduction significantly lower in the Ladymoor soil in comparison to the other four sites. This study has shown that severe metal contamination can be related to the population structure of Collembola in the field, and performance of F. candida ( in soils from such sites) in the laboratory.

Typification of farming systems for constructing representative farm models: two illustrations of the application of multi-variate analyses in Chile and Pakistan

If the fundamental precepts of Farming Systems Research were to be taken literally then it would imply that for each farm 'unique' solutions should be sought. This is an unrealistic expectation, but it has led to the idea of a recommendation domain, implying creating a taxonomy of farms, in order to increase the general applicability of recommendations. Mathematical programming models are an established means of generating recommended solutions, but for such models to be effective they have to be constructed for 'truly' typical or representative situations. The multi-variate statistical techniques provide a means of creating the required typologies, particularly when an exhaustive database is available. This paper illustrates the application of this methodology in two different studies that shared the common purpose of identifying types of farming systems in their respective study areas. The issues related with the use of factor and cluster analyses for farm typification prior to building representative mathematical programming models for Chile and Pakistan are highlighted. (C) 2003 Elsevier Science Ltd. All rights reserved.