By Robin B Matthews, William Stephens
This booklet, in keeping with paintings subsidized via the average assets structures Programme of the united kingdom division for overseas improvement, stories development in crop-soil simulation modelling and assesses its program to agriculture in constructing nations. The books is split into sections in line with a type of types with diversified features: these used as instruments via researchers; these used as instruments by way of decision-makers; and people utilized in schooling, education and expertise move. The examples used are typically from purposes of crop types in constructing nations, supplemented by way of reviews with tropical plants in Australia. the ultimate sections of the booklet speak about no matter if crop versions have proved beneficial and think about the way in which ahead in crop modelling and alertness. to be had In Print
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Extra info for Crop-soil simulation models: applications in developing countries
In another example of this approach, Chapman and Bareto (1996) used a simple model to define the extent of adaptation environments for maize in Central America using phenology and drought tolerance as traits. Monthly minimum and maximum temperature data from 364 base stations in the region were interpolated spatially in a geographical information system (GIS), and then used to develop maps of flowering date and thermal time accumulated up to 70 days after sowing (DAS). A major limitation to the use of crop models to characterize environments in this way, especially in developing countries, is the lack of input data both in spatial and temporal dimensions.
1993), for example, used the SOYGRO model to explore the influence of temperature on oil and protein content in soybean. g. , 1993) should make it easier to assess the effects of pest damage on crops, although further development is obviously needed to take into account complex mechanisms of disease resistance such as increased lignification, or changes in tissue N content. While crop models have the potential to make an important contribution to the crop improvement process, Hammer et al. (1996a) warn that there are many issues faced by plant breeders where modelling may be of limited value.
These results confirmed experimental data presented by Phadnawis and Saini (1992) for New Delhi. Hundal et al. (1999) used the CERES-RICE model to evaluate the age of seedlings at transplanting, number of seedlings per hill and plant population for rice growing in the Indian Punjab. Results showed that the optimum date of transplanting for rice was 15 June, but that earlier-transplanted (1 June) rice may perform better if seedling age is reduced from 40 to 30 days. Increasing plant population increased rice yields.