Crop modeling defines opportunities and challenges for drought escape, water capture, and yield increase using chilling-tolerant sorghum

ABSTRACTMany crop species, particularly those of tropical origin, are chilling sensitive so improved chilling tolerance can enhance production of these crops in temperate regions. For the cereal crop sorghum (Sorghum bicolor L.) early planting and chilling tolerance have been investigated for >50 years, but the potential value or tradeoffs of this genotype × management change has not been formally evaluated with modeling. To assess the potential of early-planted chilling-tolerant grain sorghum in the central US sorghum belt, we conducted CERES-Sorghum simulations and characterized scenarios under which this change would be expected to enhance (or diminish) drought escape, water capture, or yield. We conducted crop growth modeling for a full- and short-season hybrids under rainfed systems that were simulated to be planted in early (mid-April), normal (mid-May), and late (mid-June) planting dates from 1986 to 2015 in four locations in Kansas representative of the central US sorghum belt. Simulations indicated that early planting will generally lead to lower initial soil moisture, longer growing periods, and higher evapotranspiration. Early planting is expected to extend the growing period by 20% for short- or full-season hybrids, reduce evaporation during fallow periods, and increase plant transpiration in the two-thirds of years with the highest precipitation (mean > 428 mm), leading to 11% and 7% increase grain yield for short- and full-season hybrids, respectively. Thus, in this major sorghum growing region early planting could reduce risks of terminal droughts, extend seasons, and increase rotation options, suggesting that further development of chilling tolerant hybrids is warranted.

Canopy Structure and Photosynthetic Performance of Irrigated Cassava Genotypes Growing in Different Seasons in a Tropical Savanna Climate

Growth and photosynthesis performance of cassava during early vegetative growth are important determinants of final biomass. The objective of this work was to investigate canopy structure and photosynthesis performance of four cassava genotypes (Rayong 9, Rayong 11, Kasetsart 50, and CMR38-125-77) growing under irrigation at 3 and 6 months after planting (3MAP and 6MAP). Data for the 3MAP plants were collected from cassava planted on 30 June (Rainy PD), 10 November (Cool PD1), and 15 December (Cool PD2) 2015; and for the 6MAP from those planted on 20 April 2015 (Hot PD), Rainy PD, and Cool PD1. The plants growing in the rainy season had significantly higher leaf area index (LAI) than those growing in the cool and hot seasons. Consequently, they had lower percentage light penetration at the bottom of canopy, and therefore more light interception through the canopy, and hence a higher mean net photosynthesis rate (Pn) across the six canopy levels. At the 3MAP, which is the stage of maximum rate of leaf and stem growth, the Rainy PD and Cool PD2 plants of CMR38-125-77 showed the highest LAI and highest mean Pn. Similarly, the Cool PD1 plants of Kasetsart 50 showed the highest LAI and highest mean Pn. In contrast, at 6MAP during the stage of active starch accumulation in storage roots, the genotypes with the highest mean Pn were the ones having an intermediate (CMR38-125-77 for the Hot PD) or low LAI (Rayong 9 for the Rainy PD, and CMR38-125-77 for the Cool PD1). Data on variations in canopy structure and photosynthesis potentials of different cassava genotypes in response to seasonal variations may be useful for crop growth modeling and may be employed as a criterion for the selection of suitable genotypes for each growing season.

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Complete and accurate global solar radiation (Rs) data at a specific region are crucial for regional climate assessment and crop growth modeling. The objective of this paper was to evaluate the capability of 12 solar radiation models based on meteorological data obtained from 21 meteorological stations in China. The results showed that the estimated and measured daily Rs had statistically significant correlations (P<0.01) for all the 12 models in 7 subzones of China. The Bahel model showed the best performance for daily Rs estimation among the sunshine-based models, with average R2 of 0.910, average RMSE of 2.306 MJ m−2 d−1, average RRMSE of 17.3%, average MAE of 1.724 MJ m−2 d−1, and average NS of 0.895, respectively. The Bristow-Campbell (BC) model showed the best performance among the temperature-based models, with average R2 of 0.710, average RMSE of 3.952 MJ m−2 d−1, average RRMSE of 29.5%, average MAE of 2.958 MJ m−2 d−1, and average NS of 0.696, respectively. On monthly scale, Ögelman model showed the best performance among the sunshine-based models, with average RE of 5.66%. The BC model showed the best performance among the temperature-based models, with average RE of 8.26%. Generally, the sunshine-based models were more accurate than the temperature-based models. Overall, the Bahel model is recommended to estimate daily Rs, Ögelman model is recommended to estimate monthly average daily Rs in China when the sunshine duration is available, and the BC model is recommended to estimate both daily Rs and monthly average daily Rs when only temperature data are available.