Pigeonpea (Cajanus cajan (L.) Millsp.) is a slow-growing, long-cycle, N-fixing legume that originated in India and is grown extensively in the tropics and subtropics of India, Africa, and Central America,. Its seed is used for human consumption, mostly in soups/dal, especially in India.  The hulm (non-seed components) is not desired as animal feed, despite having a relatively high N concentration, and it actually decomposes more slowly than other legume residue.  It is commonly intercropped with sorghum or in rotation, and the slow residue mineralization serves a good purpose because it delays N supply to a later point in the following non-legume crops.  Because of its long life cycle and strong daylength sensitivity, traditional pigeonpea cultivars in India produce grain in November-December-January after the rainy season ends, thus providing a dry season food crop and also escaping insect pests on the grain.  It is considered drought tolerant, in part because it produces grain during the winter rabi (dry) season.  

The CROPGRO-Pigeonpea model was developed by Alderman et al. (2015), patterned after the CROPGRO-Soybean model (Boote et. al., 1998) and calibrated to two experiments:  1) intensive growth analyses including tissue N concentrations on 76W, a Florida-released cultivar, grown in Florida in 1984 (Maliro, 1987), and a less intensive growth analyses on ICPL88039, an ICRISAT-released cultivar, grown in Indore, Madhya Pradesh, India in 2003.  The Florida cultivar was selected for early flowering under long days, whereas the Indian cultivar was a very daylength-sensitive cultivar that would not have matured in Florida.  ICRISAT has developed shorter cycle cultivars of pigeonpea similar to the Florida 76W cultivar, so model users need to consider less daylength-sensitive traits for short-cycle cultivars than present in ICPL88039.  Alderman et al. (2015) employed a sequential optimization technique (software) that solved species, ecotype, and cultivar traits such as phenology timing, onset of reproductive growth, leaf area expansion, dry matter partitioning, tissue N concentrations, and photosynthetic productivity, all based on the time-series observations.   

The model requires inputs of management practices, environmental conditions and cultivar-specific traits (genetic coefficients) to predict daily growth and development (Boote et al., 1998). The species file describes characteristics assumed constant across cultivars such as tissue composition, partitioning, and sensitivity of processes to temperature, light, plant water deficit, and plant N deficiency. The required ecotype and cultivar data include lengths of developmental phases, vegetative traits, leaf traits, reference seed size, and seed composition (Jones et al., 2003).

As with the other CSM-CROPGRO models (Boote et. al., 1998; Jones et al., 2003), the pigeonpea model shares the same source code including a hedge-row light interception model (Boote and Pickering, 1994) combined with a leaf-scale photosynthesis model based on the Farquhar approach for simulating response to CO₂.  Differences from soybean include:  1) epigeal emergence and initial slow vegetative and leaf area growth, 2) much faster rate of leaf appearance on the main axis, 3) very strong sensitivity to long daylength (much more than even MG 9-10 tropical soybean types) that delays flowering until October-November in the Northern Hemisphere, and 4) tendency toward perennial behavior such that leaf N concentration remains high to maturity.  An initial evaluation of the CSM-CROPGRO-Pigeonpea model so far has been conducted for state of Karnataka, India (Sharanappa and Shivaramu, 2017; Lingaraj et al., 2019). (The CROPGRO-Pigeonpea model is relatively new, and would benefit from further evaluation in other regions and environments where it is either a major or minor crop.

References

• Alderman, P. D., K. J. Boote, J. W. Jones, and V. S. Bhatia.  2015.  Adapting the CSM-CROPGRO model for pigeonpea using sequential parameter estimation.  Field Crops Res.  181:1–15.  
• Boote, K. J., J. W. Jones, G. Hoogenboom, and N. B. Pickering.  1998.  The CROPGRO Model for Grain Legumes.  pp. 99-128.  In  G. Y  Tsuji, G. Hoogenboom, and P. K. Thornton (eds.)  Understanding Options for Agricultural Production.  Kluwer Academic Publishers, Dordrecht.
• Boote, K.J., and N.B. Pickering. “Modeling Photosynthesis of Row Crop Canopies.” HortScience 29 (1994): 1423–34. 
• Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. “The DSSAT Cropping System Model.” European Journal of Agronomy 18 (2003): 235–65.
• Maliro, C.E., 1987. Physiological aspects of yield among four legume crops under two water regimes. University of Florida, Gainesville, FL, Master’s thesis.
• Lingaraj, H., H.S. Shivaramu, M.N. Thimmegowda, and M.H. Manjunatha. 2019. Development of minimum soil and plant data set for DSSAT crop simulation model for pigeonpea cultivars under varied dates of sowing. Environment and Ecology 2019 37(3B):979-984.
• Sharanappa, K. and H.S. Shivaramu. 2017. Genetic coefficient calibration for pigeonpea cultivars in DSSAT simulation model under varied dates of sowing. Mysore Journal of Agricultural Sciences 51(2):408-413.