On Systems Thinking, Systems Biology, and the in Silico Plant

The recent summary report of a Department of Energy Workshop on Plant Systems Biology (P.V. Minorsky [2003] Plant Physiol 132: 404-409) offered a welcomed advocacy for systems analysis as essential in understanding plant development, growth, and production. The goal of the Workshop was to consider methods for relating the results of molecular research to real-world challenges in plant production for increased food supplies, alternative energy sources, and environmental improvement. The rather surprising feature of this report, however, was that the Workshop largely overlooked the rich history of plant systems analysis extending over nearly 40 years (Sinclair and Seligman, 1996) that has considered exactly those challenges targeted by the Workshop. Past systems research has explored and incorporated biochemical and physiological knowledge into plant simulation models from a number of perspectives. The research has resulted in considerable understanding and insight about how to simulate plant systems and the relative contribution of various factors in influencing plant production. These past activities have contributed directly to research focused on solving the problems of increasing biomass production and crop yields. These modeling approaches are also now providing an avenue to enhance integration of molecular genetic technologies in plant improvement (Hammer et al., 2002).

Modeling chickpea growth and development: Nitrogen accumulation and use

Quantitative information regarding nitrogen (N) accumulation and its distribution to leaves, stems and grains under varying environmental and growth conditions are limited for chickpea (Cicer arietinum L.). The information is required for the development of crop growth models and also for assessment of the contribution of chickpea to N balances in cropping systems. Accordingly, these processes were quantified in chickpea under different environmental and growth conditions (still without water or N deficit) using four field experiments and 1325 N measurements. N concentration ([N]) in green leaves was 50 mg g-1 up to beginning of seed growth, and then it declined linearly to 30 mg g-1 at the end of seed growth phase. [N] in senesced leaves was 12 mg g-1. Stem [N] decreased from 30 mg g-1 early in the season to 8 mg g-1 in senesced stems at maturity. Pod [N] was constant (35 mg g-1), but grain [N] decreased from 60 mg g-1 early in seed growth to 43 mg g-1 at maturity. Total N accumulation ranged between 9 and 30 g m-2. N accumulation was closely linked to biomass accumulation until maturity. N accumulation efficiency (N accumulation relative to biomass accumulation) was 0.033 g g-1 where total biomass was -2 and during early growth period, but it decreased to 0.0176 g g-1 during the later growth period when total biomass was >218 g m-2. During vegetative growth (up to first-pod), 58% of N was partitioned to leaves and 42% to stems. Depending on growth conditions, 37-72% of leaf N and 12-56% of stem N was remobilized to the grains. The parameter estimates and functions obtained in this study can be used in chickpea simulation models to simulate N accumulation and distribution.

Sorghum Crop Modeling and Its Utility in Agronomy and Breeding

Crop models are simplified mathematical representations of the interacting biological and environmental components of the dynamic soil–plant–environment system. Sorghum crop modeling has evolved in parallel with crop modeling capability in general, since its origins in the 1960s and 1970s. Here we briefly review the trajectory in sorghum crop modeling leading to the development of advanced models. We then (i) overview the structure and function of the sorghum model in the Agricultural Production System sIMulator (APSIM) to exemplify advanced modeling concepts that suit both agronomic and breeding applications, (ii) review an example of use of sorghum modeling in supporting agronomic management decisions, (iii) review an example of the use of sorghum modeling in plant breeding, and (iv) consider implications for future roles of sorghum crop modeling. Modeling and simulation provide an avenue to explore consequences of crop management decision options in situations confronted with risks associated with seasonal climate uncertainties. Here we consider the possibility of manipulating planting configuration and density in sorghum as a means to manipulate the productivity–risk trade-off. A simulation analysis of decision options is presented and avenues for its use with decision-makers discussed. Modeling and simulation also provide opportunities to improve breeding efficiency by either dissecting complex traits to more amenable targets for genetics and breeding, or by trait evaluation via phenotypic prediction in target production regions to help prioritize effort and assess breeding strategies. Here we consider studies on the stay-green trait in sorghum, which confers yield advantage in water-limited situations, to exemplify both aspects. The possible future roles of sorghum modeling in agronomy and breeding are discussed as are opportunities related to their synergistic interaction. The potential to add significant value to the revolution in plant breeding associated with genomic technologies is identified as the new modeling frontier.