Integrated, participatory, seasonal observations for land systems
Seasonality is now accepted as a central concern in the proverty and underdevelopment problem in LDCs. (Chambers et al, 1981 and Longhurst, 1986). There have been attempts to analyze and explain the relationship between seasonality and employment, (under) nutrition, poverty, health, working capacity / stress, and gender bias. But little effort has been made to translate these explanations / conclusions to tangible counter-seasonal development strategies. On the contrary, several development projects and programmes have aggravated the seasonality problem in LDCs, thereby bringing more misery to rural areas, especially to the poorest households. In this context, Chambers (1995) has been vociferous in demanding “a paradigm of reversals and altruism”, one in which the rural poor have the right to conduct their own analysis. Several conceptual and action oriented changes or reversals have been suggested.(Chambers 1995). In this paper we present systems concepts and the generation and utilization of information within the framework of seasonality, as the conceptual and action oriented changes that can lead to sustainable agricultural development.
One crucial input that can help rural people conduct their own analysis is their access to the information and data about themselves. We present seasonality, or the existence of the rural poor “in-time” unlike our “season-proof” existence, as the principle around which the information base for the rural poor can be built. This calls for a season-sensitive information base.
The case for seasonal land observations
Seasonality, in the context of developed countries, is not a problem in itself. In the LDCs, poverty (low mean income) and seasonality (high variance), leads to a critical minimum level of consumption. This, the low mean high variance income matrix gives us the basic framework within which rural development action is located. Poverty, in most LDCs is not determined by income (cash and kind) alone. Several other factors like caste, traditional occupations, social contracts, and labour relationships, influence income, employment and consumption decisions. Poverty, the low mean income, does add to this nexus of problems, determining the adjustments / adaptations that people make to seasonal changes.
What according to the rural poor, is the most preferred and effective counter-seasonal strategy? Jodha (1988) highlighted the “development paradox” (also called “Jodha’s paradox” -See Chambers 1995). Villagers in Rajasthan, on defining their own categories and criteria of economic status, scored themselves better off but poorer in 1982-84 than in 1964-66. The paradox came with their criteria, 37 out of 38, which revealed that they were on the average better off despite being poorer in terms of per capita real incomes. Another participatory analysis (Chambers 1995, p. 16) in Pakistan revealed that “more income” was the 9th or 10th preferred in a list of some 20 criteria. Coming back to Jodha’s development paradox, and the criteria of well-being listed by the villagers, it is obvious that all these criteria relate directly or indirectly to the seasonality of their existence. An increase in well-being is a function of a decline in the seasonality of existence. To understand the seasonal problems and well being, and to design appropriate and feasible counter-seasonal development projects, it is essential to perceive changes in seasonality and well-being as part of the overall system dynamics of the village. Land being the most critical resource for food production and for employment, we consider how information on land is used in a counter-seasonal development project.
In India, the State Agricultural Statistics Authorities generate data on agricultural and livestock production at the State level, which are aggregated by the Directorate of Economics and Statistics of the Central Ministry of Agriculture. Given the processes of collection and the treatment of inputs as common to both sectors, the Gross Domestic Product figures are not available separately for the agriculture and livestock sub-sectors. (Kulshreshtha, 1997, p. 1652). In each State the crop yield estimates by the General Crop Estimation Surveys (GCES) is prepared crop-wise, based on scientifically designed crop cutting experiments. The GCES is dependent on timely completion of basic records; the theoretical design of GCES is disturbed by incomplete primary land use statistics. (ibid, p.1653) Given the difference in sowing time (ranging about two months during each season) even within the same agro-ecological zone in a State (there may be 5 - 12 agro-ecological zones in a State) the problems of timely reporting of yield or non-completion of yield statements, are major drawbacks and important sources of non-sampling error in the estimation of crop yield in each season (Kharif and Rabi). (ibid , Table 5) Agricultural statistics in India is sufficiently decentralized in terms of location or points of data collection. The centralization is in terms of time; the data on area sown (land use statistics), crop yield (GCES), prices (value of output) are all collected at specific points of time for each for the entire district. A district often covers different agro-ecological zones, with sowing dates, harvest, and market transactions ranging over two months for each, even within the same agro-ecological zone. The likelihood of over or underestimation of domestic product from the agricultural sector is significant. In the case of prices, secondary data is particularly insensitive to the actual farm income; “the output of a district is treated as transacted at the average price prevailing in different primary marketing centres during the peak marketing period in a district.” (ibid, p. 1654). Given the size of the country and the enormous coverage of the agriculture and livestock sectors, it is perhaps difficult to collect farm gate prices (the first point of transaction) at regular intervals during the harvest season in each agro-ecological zone, with statistically accurate aggregations for estimation of volume and value of produce transacted at each level, district, State and national. But if the problems associated with the seasonality of rural livelihoods are to be solved, the information has to be meaningfully decentralized and disaggregated both in space and time - location-wise and season-wise. Information across institutions and through intra- and inter-seasonal intervals in time, is essential if a data-base for regional planning is to be built. The institutions include, depending on the specific eco-regional context, individuals / farms/ households, to village clusters / agri-business groups and labour contracts / caste systems.