Combining Simple Phenotyping and Photothermal Algorithm for the Prediction of Soybean Phenology: Application to a Range of Common Cultivars Grown in Europe

Abstract

Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology under different temperature and photoperiod conditions. For that purpose, a simple phenology algorithm (SPA) was developed and parameterized for 10 contrasting soybean cultivars (maturity group 000 to II). Two experiments were carried out at INRA Toulouse (France) for parameterization: 1) Phenological monitoring of plants in pots on an outdoor platform with 6 planting dates. 2) Response of seed germination to temperature in controlled conditions. Multi-location field trials including 5 sites, 4 years, 2 sowing dates, and 10 cultivars were used to evaluate the SPA phenology predictions. Mean cardinal temperatures (minimum, optimum, and maximum) for germination were ca. 2, 30, and 40°C, respectively with significant differences among cultivars. The photoperiod sensitivity coefficient varied among cultivars when fixing Popt and Pcrt, optimal and critical photoperiods respectively, by maturity group. The parameterized algorithm showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity) in the field trials including 75 data points. Flowering (R1 stage), and beginning of grain filling (R5 stage) dates were satisfactorily predicted with RMSEs of 8.2 and 9.4 days respectively. Because SPA can be also parameterized using data from field experiments, it can be useful as a plant selection tool across environments. The algorithm can be readily applied to species other than soybean, and its incorporation into cropping systems models would enhance the assessment of the performance of crop cultivars under climate change scenarios.

Soybean Nodulation Response to Cropping Interval and Inoculation in European Cropping Systems

“…There are studies to identify potent indigenous and commercial Bradyrhizobia inoculants suitable for European conditions (Zimmer et al, 2016;Yuan et al, 2020) and attempt to incorporate soybeans in the no-till cover-crop system (Halwani et al, 2019). However, very little is known about the potential of soybeans in higher latitudes (Lamichhane et al, 2020;Schoving et al, 2020). Soybean seeds contain high nutrients for feed and food use, and the crop establishes nitrogen-fixing symbiosis with Bradyrhizobia, fixing 68% and 119 kg ha −1 of nitrogen aboveground from the atmosphere (Peoples et al, 2009).…”

Combining Experimental and Modeling Approaches to Understand Genotype x Sowing Date x Environment Interaction Effects on Emergence Rates and Grain Yield of Soybean

“…Second, unlike crops such as sunflower or maize, soybean has a high degree of plasticity in vegetative traits and thus is capable of compensating seedling emergence losses due to its semi-determinate or indeterminate growth habit and ramification capacity ( Vega and Sadras, 2003 ; Andrade and Abbate, 2005 ). Indeed, the cultivar Isidor used in this study is characterized by semi-determinate growth while cultivars Ecudor and Santana are characterized by indeterminate growth ( Schoving et al, 2020 ). However, the rate of soybean emergence may significantly impact grain yield when a lower density than used herein is adopted and when cultivars with determinate growth habit is used ( Vega and Sadras, 2003 ).…”

“…Consequently, we used from our database ( Lamichhane et al, 2020a ) the parameter values related to hypocotyl and radicle elongation, and seedling mortality rates under soil aggregates for the cultivars used in this study. In contrast, we estimated parameters of the model related to seed germination at different temperatures and water potentials of all three soybean cultivars used in this study that have contrasting characteristics ( Schoving et al, 2020 ): cv. Ecudor belongs to the maturity group II (MGII), is characterized by indeterminate growth, and has big round leaves; cv.…”

“…Summer drought in these regions has been reported as the main limiting factor that affect the flowering and seed filling phase, critical for soybean yield ( Maury et al, 2015 ). In contrast, an early sowing, as compared with conventional sowing dates, may provide higher crop yield in two ways across these regions ( Schoving, 2020 ; Schoving et al, 2020 ): (i) by allowing soybean to produce more pods and seeds due to early flowering, at a less advanced vegetative stage and (ii) by allowing the crop to escape water deficit around flowering and seed filling phase. At the same time, the emergence quality of soybean can be affected due to shifting in sowing dates, especially for early sowings, which increase crop exposure to lower soil temperatures.…”

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

“…The infection of this pathogen is observed on all parts of the plant i.e., branches, leaves, pods, petioles, root, stem, and seeds on soybean ( Gupta et al, 2012 ), however, the key indications of disease are observed after the flowering stage in infected plants, i.e., R7 stage, especially in low humidity level and high-temperature conditions ( Schoving et al, 2020 ). Chlorosis of leaves, premature defoliation, and reduced vigor are the major symptoms observed in the infected plants ( Romero Luna et al, 2017 ), which result in reduced productivity, sterility of pod, and formation of crinkled and tiny seeds.…”