The CSM-CROPGRO-Perennial-Forage model was released in DSSAT V4.7, complete with species adaptations and data files for three perennial forages: Brachiaria brizantha Stapf cv. Marandu, ‘Tifton 85’ bermudagrass (Cynodon spp.) from Pequeno et al. (2017), and alfalfa (Medicago sativa L.) cv. Aragón dormancy group 7 (Malik et al., 2018).
Development of the Perennial Forage Model: The CROPGRO Perennial Forage Model (CROPGRO-PFM) was developed from the annual crop version of CROPGRO by Rymph (2004) in order to allow simulations with periodic partial to complete defoliation and subsequent regrowth using newly added state variables for storage of carbohydrate and nitrogen reserves in a new storage tissue type (stolon, rhizome, crown, taproot), along with rules for use and re-fill of those reserves for re-growth even after complete defoliation or surface winter-kill. From 2004 until recently, this model version had remained as a standalone model outside of the DSSAT shell, even while its species, ecotype, and cultivar files were adapted to simulate growth responses of several perennial forage genotypes such as Paspalum notatum Flüegge cv. Pensacola (Rymph, 2004; Rymph et al., 2004), Brachiaria brizantha Stapf cv. Xaraés (Pedreira et al., 2011), Panicum maximum Jacq cv. Tanzânia (Lara et al., 2012), Brachiaria brizantha Stapf cv. Marandu (Pequeno et al., 2014), ‘Tifton 85’ bermudagrass (Cynodon spp.), and Brachiaria hybrid cv. Mulato II (CIAT 36061) (Pequeno et al., 2017). In 2017, the CROPGRO-PFM was brought into the code and shell of DSSAT V4.7 release, with species parameterization and data files for Brachiaria brizantha Stapf cv. Marandu and ‘Tifton 85’ bermudagrass (Cynodon spp.) from Pequeno et al. (2017) and alfalfa (Medicago sativa L.) cv. Aragón dormancy group 7 (Malik et al., 2018).
How is the model different from CROPGRO? It is perennial, with a storage organ that facilitates re-growth after periodic harvest to as low as zero LAI along with dormancy and over-wintering ability. It has additional state variable code related to the storage organ and associated carbohydrate and N pools. The model also has additional parameterization for partitioning to storage tissues, along with mobilization and re-fill rules for those tissues. Additional outputs include herbage mass (different from aboveground mass) as well as percent leaf and crude protein of herbage. Those familiar with DSSAT may recognize existing bahiagrass and brachiaria simulations with the annual CROPGRO version. We have low confidence in the annualized versions and do not recommend using them.
How is CROPGRO-PFM similar to the annual CROPGRO model? It has the same leaf to canopy photosynthesis capabilities, and we presently recommend only the L version (hourly leaf level) because it can be parameterized from physiological measurements of leaf photosynthesis. While the C-3 photosynthesis version is similar, the CROPGRO-PFM has an added feature to mimic C-4 photosynthesis in a simple way via modified specificity ratio of carboxylase for CO2 over O2. The two models are similar in their water balance, energy balance, N balance and both have N-fixing and non-legume capabilities. The alfalfa model version demonstrates very considerable N-fixation.
Plans for future: We plan additional improvement of the newly-released alfalfa model in the next year or two facilitated by our involvement in the grant project of Isaya Kisekka (PI, Univ. of California, A Decision Support Tool for Predicting Alfalfa Yield and Quality to Enhance Resource Use Efficiency). That project will improve producer utilization of the model as a DSS Tool. We plan to add additional perennials including bahiagrass (Paspalum notatum cv. Pensacola) and Cynodon sp cv. Jiggs. We will add a version for annual ryegrass (in progress). We have explored simulation of cellulosic biofuel crops such as Napier Hybrid that are harvested once per year which require very different species parameterization. We are interested to collaborate with researchers who may have data sets on C-3 perennial grasses. We are adding digestibility and considering linkages to animal grazing (interactive animal models).
Lara, M.A.S., C.G.S. Pedreira, K.J. Boote, B.C. Pedreira, L.S.B. Moreno, and P.D. Alderman. 2012. Predicting growth of Panicum Maximum: an adaptation of the CROPGRO-perennial forage model. Agronomy J. 104: 600–611.
Malik, W., K. J. Boote, G. Hoogenboom, J. Cavero, and F. Dechmi. 2018. Adapting the CROPGRO model to simulate alfalfa growth and yield. Agronomy J. 110 (accepted, in press).
Pedreira, B.C., C.G.S. Pedreira, K.J. Boote, M.A.S. Lara, and P.D. Alderman. 2011. Adapting the CROPGRO perennial forage model to predict growth of Brachiaria brizantha. Field Crops Res. 120: 370–379.
Pequeno, D.N.L., C.G.S. Pedreira, and K.J. Boote. 2014. Simulating forage production of Marandu palisade grass (Brachiaria brizantha) with the CROPGRO-Perennial Forage Model. Crop and Pasture Science 65:1335–1348.
Pequeno, D. N. L., C. G. S. Pedreira, K. J. Boote, P. D. Alderman, and A. F. G. Faria. 2017. Species-genotypic parameters of the CROPGRO Perennial Forage Model: Implications for comparison of three tropical pasture grasses. Grass and Forage Science 2017: 1-16.
Rymph, S.J. 2004. Modeling growth and composition of perennial tropical forage grass. PhD in Agronomy, University of Florida, Gainesville, p.316
Rymph, S.J., K.J. Boote, A. Irmak, P. Mislevy, and G.W. Evers. 2004. Adapting the CROPGRO model to predict growth and composition of tropical grasses: developing physiological parameters. Soil and Crop Science Society of Florida Proceedings, Gainesville 63:37–51.