Yield and yield components of rice were analyzed in a total of 113 paddy fields in a typical rice-growing village in the central plain of Laos in order to determine how rain-fed lowland rice production could be improved. Average grain weight decreased significantly from 242.3 g m-2 in 2005 to 154.1 g m-2 in 2006 due to low rainfall in June, August and September. High-yield plots were located near the settlement or drainage channels in both years, and received water from the settlement or drainage channels. All the yield component values, as well as straw weight, decreased significantly in 2006 except for 1000-grain weight. The variations in yields and yield components were extremely large, especially in 2006. Plots with small numbers of spikelets per m2 and a low percentage of ripened grains were observed in 2006. These plots tended to be transplanted later, mainly in August, and were located far from the settlement. The variation in grain weight was closely related to the straw weight, number of spikelets per m2 and number of spikelets per panicle in both years. Correlation coefficient analysis suggested that the higher volumetric water content of surface soil and the planting of improved cultivars were more effective for increasing grain weight than fertilizer application, soil properties, transplanting period, growth duration or plot altitude.
NERICA (New Rice for Africa) is expected to be a driving force of the green revolution in Sub-Saharan Africa, where most farmers practice risky, unstable upland agriculture that depends on rainfall. At this juncture, a critical question to be asked is what the real potential of NERICA is, if introduced in upland farming in the region, and if not only higher yield but also higher risks are taken into account. In this paper, we try to give an answer to this question through studying how the introduction of NERICA changes farmers’ optimum cropping patterns. We use a profit optimization model to determine optimum land use for three major staple crops, i.e., NERICA, maize and millet, planted in Uganda, where most crop cultivation is carried out on sloping fields that characterize Ugandan agriculture. The results of simulations show that the upper and lower parts of slopes have very different optimum cropping patterns because the risk associated with NERICA yield is sharply different between the two parts. A rapid increase in area planted to NERICA is desirable in the lower parts of slopes, where NERICA has significant advantage. In the upper parts of slopes it is not optimal to increase area planted to NERICA beyond the areas planted to maize and millet. NERICA dissemination policies, which have thus far overlooked information about the locations of farm fields, could be made more effective by taking such information into account.
The purpose of the present study was to develop two modeling frameworks to predict suitable areas for sugarcane (Saccarum officinarum) cultivation in Sri Lanka using Geographical Information Systems (GIS). The first approach consisted of neural network-based GIS modeling and the second approach consisted of GIS-based cartographic modeling. Soil properties, meteorological data, current vegetation and slope accessibility were considered to be major factors to identify potential lands for sugarcane cultivation. The Levenberg-Marquardt (LM) algorithm was used to develop the Artificial Neural Network (ANN) model and the Normalized Weighting Method was used to obtain the weight values for the cartographic model. The results showed that the highly suitable lands obtained from the two models were differed by 7% in the current study area. According to the final suitability map obtained from the ANN model, 17.24%, 29.74% and 23.71% of the lands were classified into highly, moderately and marginally suitable categories, respectively. The results of the weighted overlay model showed that 10.34%, 32.33% and 28.02% of the lands corresponded to highly, moderately and marginally suitable categories, respectively. Neither model enabled to identify ‘unsuitable’ land parcels. Cartographic modeling did not enable to handle noisy and missing data. It was concluded that both approaches have their advantages and drawbacks for different purposes. However, these results revealed that neural network-based GIS modeling could become a powerful alternative approach towards automated spatial decision-making.