4. Discussion
In this paper it is proposed to use crop genotype selection and farming region control for irrigation
optimization and improving water sustainability. A tool is developed for offering irrigation demand and
harvest amount information for three dimensional scenarios: diverse genotypes, multiple farming regions,
and a long timeframe. A preliminary study and analysis are carried out on a crop sunflower with 20
genotypes, 25 European farming regions, and over 150 years, in order to demonstrate the proposition and
to test the tool. This study results in a methodology usable in future research, to study interactions
between irrigation, crop genotypes and the environment.
Our results are very encouraging. Firstly, this kind of simulation offers a large number of characteristic
features for various contrasting scenarios. Moreover, for diverse genotypes and farming regions, the
simulation results predict a variety of distinct irrigation demands and harvest amounts. This indicates that
appropriately selecting a combination of these parameters can result in improved results. Secondly, these
simulations qualitatively produce a good fit to real data. This suggests that it is reliable to use simulated
information and that we may have confidence in our analysis. Lastly, rules to achieve optimal results are
explored. Particular analyses proved that proper selection of the genotype and farming region may save a
considerable amount of water for irrigation. For concrete policy decisions, selection rules integrate the
project objective, the yield requirements, the irrigation budget, and the technological level available in the
target location. Using the dynamic system formulation of the plant growth model, the tool develops
numerical optimisation techniques to determine multi-constraint optimal farming strategies [10] [11].
4. DiscussionIn this paper it is proposed to use crop genotype selection and farming region control for irrigationoptimization and improving water sustainability. A tool is developed for offering irrigation demand andharvest amount information for three dimensional scenarios: diverse genotypes, multiple farming regions,and a long timeframe. A preliminary study and analysis are carried out on a crop sunflower with 20genotypes, 25 European farming regions, and over 150 years, in order to demonstrate the proposition andto test the tool. This study results in a methodology usable in future research, to study interactionsbetween irrigation, crop genotypes and the environment.Our results are very encouraging. Firstly, this kind of simulation offers a large number of characteristicfeatures for various contrasting scenarios. Moreover, for diverse genotypes and farming regions, thesimulation results predict a variety of distinct irrigation demands and harvest amounts. This indicates thatappropriately selecting a combination of these parameters can result in improved results. Secondly, thesesimulations qualitatively produce a good fit to real data. This suggests that it is reliable to use simulatedinformation and that we may have confidence in our analysis. Lastly, rules to achieve optimal results areexplored. Particular analyses proved that proper selection of the genotype and farming region may save aconsiderable amount of water for irrigation. For concrete policy decisions, selection rules integrate theproject objective, the yield requirements, the irrigation budget, and the technological level available in thetarget location. Using the dynamic system formulation of the plant growth model, the tool developsnumerical optimisation techniques to determine multi-constraint optimal farming strategies [10] [11].
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