Development of a spatial planning support system for agricultural policy formulation related to land and water resources in Borkhar & Meymeh district, Iran

In this study, a system was developed to support agricultural planners and
policy makers in land resource analysis, policy formulation, identification of
possible policy measures and policy impact analysis. The research is part of a
larger programme, aiming at development of a model system to support
agricultural policy formulation at national level. The current study focused on
methodology development and its implementation in Borkhar & Meymeh
district in Esfahan province, Iran.
The system comprises three main components, i.e. resource analysis, policy
impact assessment and policy evaluation. The biophysical resource analysis was
carried out using CGMS, the Crop Growth Monitoring System which includes
WOFOST, a generic crop growth simulation model. This model simulates
growth of annual crops in the potential and water-limited production situations,
based on daily weather data, crop characteristics and soil physical
characteristics. For this purpose, crop characteristics of winter wheat and winter
barley were calibrated based on research data from the agro-meteorological
research center of Kaboutar Abad, Esfahan, Iran. Crop characteristics of silage
maize, sugar beet, sunflower and potato were calibrated based on yields of the
best agricultural producers in the region.
For the weather stations in which solar radiation was not measured, it was
estimated from sunshine-hours or temperature, using empirical relations. A
sensitivity analysis on method of solar radiation estimation was carried out to
test model performance in terms of simulated crop yield and water requirements
for winter barley and sugar beet as representatives of winter and summer crops,
respectively. Results of this analysis showed that the maximum difference in
simulated crop yield based on estimated and measured solar radiation is less
than 10%.
CGMS was used for land resource analysis at the regional (district) scale. The
potentially suitable area for agriculture in the district was identified and
classified into 128 homogenous units (referred to in this study as Elementary
Mapping Units, EMU) in terms of soil, weather and administrative unit. For
each EMU, soil physical characteristics were derived from available soil maps
and soil analyses reports. Daily weather characteristics (maximum and
minimum temperature, vapor pressure, wind speed, rainfall, and solar radiation)
were generated for the centre of each EMU by interpolation of daily weather
data of 33 weather stations, located in and around the district. CGMS was then
modified to allow calculation of irrigated crop yields. Yields of major crops and
water requirements per decade were simulated using CGMS for three irrigation
regimes (full irrigation, 20% and 40% deficit irrigation). Fertilizer requirements
for the three macro-nutrients, nitrogen, phosphorus and potassium, for each
level of crop production were estimated based on soil chemical characteristics,
crop yields and nutrient content in economic crop products and crop residues.
An alternative methodology was developed for spatial estimation of crop yields,
water and fertilizer requirements of crops (alfalfa, melon, watermelon, and
colza) that could not be simulated by CGMS, either because of model
limitations or lack of data for model calibration. The ratio of current and
potential crop yields, referred to as production efficiency, was used as an
indicator of management ability of farmers and was used in farm classification.
The policy formulation process consists of three steps: i) selection of policy
objectives, ii) identification of policy instruments and iii) assessment and
analysis of their impacts. In this study, policy objectives and relevant policy
instruments were derived from the latest agricultural development documents. A
model was developed to assess the impacts of policy instruments and another
model for analysis of these impacts from different perspectives. As reactions of
farmers to policy instruments may be different, depending on their socioeconomic
situation and the biophysical characteristics of their land, a planning
(modelling) unit was defined, homogenous in terms of biophysical and socioeconomic
characteristics. For this purpose, farms belonging to each of the
agricultural production systems (e.g., traditional, cooperative and agroindustrial)
were classified into farm types, based on land and water availability,
overall production efficiency and average net income per ha. These farm types
were combined with land units to form the basic units of analysis, i.e. farm
type-land units (FTLU), homogenous in terms of biophysical potential, as well
as in resource endowments and management ability of farmers.
A distributed linear programming model was developed to assess policy
impacts by simulating the response of the various farm types to specific policy
instruments. This model is optimizing a utility function, composed of a
combination of net income and production cost, subject to various constraints at
different spatial scales (e.g., farm type-land unit, farm type, village, and subdistrict).
The model was validated based on the conditions of the year 2002-03
by comparing simulated crop yields and total crop production in Borkhar subdistrict
with detailed agricultural census data. Indicators, representing the
effect/impact of policy instruments on economic, social, and environmental
objectives of various stakeholders were selected and quantified in a post-model
analysis.
In a model experiment, the reactions of the different farm types to three policy
instruments, aiming at increasing agricultural water productivity in Borkhar
sub-district were simulated. A multi-criteria evaluation technique was used for
policy analysis through overall assessment of the various economic, social and
environmental indicators to evaluate the effectiveness of various policy
instruments.
The developed system represents a further step in the development of computeraided
decision support systems for land use analysis that have received ample
attention in the research community, in response to the perceived needs of
policy makers. The consultations with planners in the course of the study, leads
to the conclusion, however, that still a long way has to be gone to bridge the gap
between the policy makers that are asking questions that land use modelers can
not answer and the land use modelers that are generating answers to questions
that policy makers are not (willing to) ask(ing).