Soybean (Glycine max) is widely grown for its oil and protein-rich seed, which are consumed directly by humans as a vegetable and in processed forms and by poultry and livestock as various processed feed components. It is among the five most important crops globally. As a legume, the crop can fix nitrogen via symbiosis so is also prized for soil fertility benefits in rotations, especially with corn.

The soybean module of CROPGRO was based on the model SOYGRO V4.2 (Wilkerson et al., 1983) but has been substantially modified from the original version (Hoogenboom et al., 1992; Jones et al., 2003). Growth is driven by 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  (Alagarswamy et al., 2006). Vegetative and reproductive development is driven by temperature and photoperiod calculated at an hourly time step (Boote et al., 1998).  The genetic coefficients for reproductive development are well-parameterized for the full range of soybean maturity groups from 00 to X (Grimm et al., 1993, 1994; Piper et al., 1998).

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 (Boote et al., 2001; Jones et al., 2003).

As a process-oriented model, CSM-CROPGRO-Soybean can be used to study soybean response to management (Egli and Bruening, 1992; Boote et al., 1997), crop rotation (Alagarswamy et al., 2000; Singh et al., 1999a, b; Liu et al., 2013), environmental conditions (Banterng et al., 2010; Curry et al., 1995; Nielsen et al., 2002; Sau et al., 1999; Ruiz-Nogueira et al., 2001), and varietal response (Mavromatis et al., 2001, 2002), including genetic yield potential (Boote and Tollenaar, 1994; Boote et al., 2003). This includes causes of spatial yield variability (Paz et al., 1998; Nijbroek et al., 2003), remote sensing applications (Richetti et al., 2019), and potential impacts of climate change (Bao et al., 2015 a, b; Boote, 2011; Boote et al., 2018; Heinemann et al., 2006).


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