The SSM (Simple Simulation Modelling) (https://sites.google.com/site/cropmodeling/cyrus-chickpea) is a family of mechanistic crop models (chickpea version in the previous link), which uses daily weather information as input to simulate crop growth and development from sowing to maturity. The two main factors influencing plant growth are temperature and light. Temperature data are converted by the crop model into thermal units, which are used to predict the number of leaves that are generated. The thermal time between the emission of two consecutive leaves is a key coefficient. Temperature is also the factor that guides the development of phenological stages like time to flowering, beginning of seed growth and duration of seed growth.
The first target in genotype parameterization with SSM is to set the phenological stages right and the phyllochron.
The second stage is to measure the canopy development, since light interception will be dependent on the size of the canopy. Two key coefficients are those involved in the power function between the leaf number on the main stem (i.e. node number NN, which is dependent on the phyllochron), and the leaf area (LA = a*NNb). The coefficient that is used to convert the intercepted light into biomass is the Radiation Use Efficient (RUE), which is usually obtained from literature data, but for which there is also important genetic variation.
The third stage is to collect a number of plant parameters (see ‘Crop’ sheet in standard model, and ‘Help’ sheet for a description of them) that, as a beginning, can be taken as generic for each species and are obtained from literature data. This is followed by partitioning of biomass produced between leaves and the other plant parts (note that the partitioning coefficient changes with phenological stages, i.e. before and after the cessation of leaf growth on the main stem). There are a series of coefficients that reflect the response to soil drying (biomass accumulation, leaf expansion, symbiotic nitrogen fixation, phenological development). A number of leaf and stem coefficients such as the specific leaf weight, or N content in leaf and stem at different phenological stages (important as these reflect N pools), coefficient on root growth (rate of depth increase of a root front, in cm per thermal unit) and coefficients on the transpiration response to increasing VPD (sensitivity TRUE/FALSE – threshold value) are required.
For us gems means GEMS, or G*E*M*S (genotype by environment by management by society) interactions, i.e. the fact that crop yields results from complex biophysical interactions while acceptance depends on farmer/consumer preferences. This complexity becomes an opportunity when it is cracked into components that can be analysed, understood, predicted, and then used to prioritise research investments to maximise return. This is what we do, and this is when GEMS become gems!
For us gems means GEMS, or G*E*M*S (genotype by environment by management by society) interactions, i.e. the fact that crop yields results from complex biophysical interactions while acceptance depends on farmer/consumer preferences. This complexity becomes an opportunity when it is cracked into components that can be analyzed, understood, predicted, and then used to prioritize research investments to maximise return. This is what we do, and this is when GEMS become gems!
A crop performs in different ways in different sites, years and agronomic managements. These are called genotype-by-environment-by management(G*E*M) interactions, and they are a main challenge for breeders and agronomists. There is one more layer of interaction, even more complex: the society (S). Farmers and consumers have different desires, needs, expectations, and a cultivar that fits one may not fit the other (G*E*M*S interactions). The puzzle is complex and challenging but if its components are understood, specific interventions can be undertaken.For instance, breeding for a particular genotype (G, with particular physiological characteristics), for a particular environment (E, with a particular kind of drought pattern that requires a specific adaptive trait), in a particular management practice (M, for instance a sowing density, or a N fertilizer treatment), and targeted to particular farmer/consumer (S, for instance a genotype that produces a lot of rich stover for cattle ranchers) is the need of the hour.