Advances in Crop Simulation Modelling

In the context of the Chinese government’s policy guidance, there is black soil protection and ecological environment protection. The purpose of this paper is to solve the problem that the soil ecology of the black soil in Northeast China is changing year by year, and it is necessary to explore the sowing and fertilization strategy under the new situation; most Chinese growers rely excessively on their personal experience in the process of soybean sowing and fertilization. In this study, we used “Heihe 43” soybeans and used regression experimental design methods to analyze the effects of planting density, nitrogen, phosphorus, and potassium fertilizer application on soybean yield and to determine the optimal planting density and fertilizer ratios. The study reveals that the optimal soybean planting density in Northeast China is 45.37 × 10⁴ plants/ha, with nitrogen at 98.4 kg/ha, phosphorus at 218.96 kg/ha, and potash at 47.62 kg/ha. Under these conditions, soybean yields can reach 3816.67 kg/ha. This study can provide a theoretical method for decision-making to obtain the optimal planting density and fertilizer ratio for different regions of the farming system. Full article

Crop growth monitoring plays an important role in estimating the scale of food production and providing a decision-making basis for agricultural policies. Moreover, it can allow understanding of the growth status of crops, seedling conditions, and changes in a timely manner, overcoming the disadvantages of traditional monitoring methods such as low efficiency and inaccuracy. In order to realize rapid and non-destructive monitoring of winter wheat growth status, this study introduced an equal weight method and coefficient of variation method to construct new comprehensive growth indicators based on drone images and measured data obtained from field experiments. The accuracy of the indicators in evaluating the growth of winter wheat can be judged by the construction, and the effects of different machine learning methods on the construction of indicators can be compared. Correlation analysis and variable screening were carried out on the constructed comprehensive growth indicators and the characteristic parameters extracted by the drone, and the comprehensive growth index estimation model was constructed using the selected parameter combination. Among them, when estimating the comprehensive growth index (CGIavg), the optimal model at the jointing stage is the support vector regression (SVR) model: R2 is 0.77, RMSE is 0.095; at the booting stage, the optimal model is the Gaussian process regression (GPR) model: R2 is 0.71, RMSE is 0.098; at the flowering stage, the optimal model is the SVR model: R2 is 0.78, RMSE is 0.087. When estimating the comprehensive growth index based on the coefficient of variation method (CGIcv), the optimal model at the jointing stage is the multi-scale retinex (MSR) model: R2 is 0.73, RMSE is 0.084; at the booting stage, the optimal model is the GPR model: R2 is 0.74, RMSE is 0.092; at the flowering stage, the optimal model is the SVR model, R2 is 0.78: RMSE is 0.085. The conclusion shows that the method of constructing the comprehensive growth index is superior to the function of a single parameter to some extent, providing a new way for wheat growth monitoring and process management. Full article

Spring barley (Hordeum vulgare L.) is an increasingly important cash crop in the province of Quebec (Canada). Soil–crop models are powerful tools for analyzing and supporting sustainable crop production. STICS model has not yet been tested for spring barley grown over several decades. This study was conducted to calibrate and evaluate the STICS model, without annual reinitialization, for predicting aboveground biomass and N nutrition attributes at harvest during 31 years of successive cropping of spring barley grown in soil (silty clay, Humic Gleysol) from the Saguenay–Lac-Saint-Jean region (northeastern Quebec, Canada). There is a good agreement between observed and predicted variables during the 31 successive barley cropping years. STICS predicted well biomass accumulation and plant N content with a low relative bias (|normalized mean error| = 0–13%) and small prediction error (normalized root mean square error = 6–25%). Overall, the STICS outputs reproduced the same trends as the field-observed data with various tillage systems and N sources. Predictions of crop attributes were more accurate in years with rainfall close to the long-term average. These ‘newly calibrated’ parameters in STICS for spring barley cropped under continental cold and humid climates require validation using independent observation datasets from other sites. Full article

Steroidal glycoalkaloids (SGAs) are secondary metabolites that are closely associated with the sensory and processing qualities of potato tubers. GGE biplots are a widely used tool for analyzing crop breeding analysis. This study aimed to investigate the effect of light on SGA biosynthesis by employing GGE biplots to analyze the transcriptional gene expression of seven genes involved in the SGA biosynthesis pathway. Tubers of five different potato genotypes were incubated for 6, 12, and 24 h under red light. The expression levels of the seven genes were measured using qRT-PCR for analysis. Further analysis of the data was performed using GGE biplots. Our results indicated significantly higher expression levels for Pvs1, Sgt1, and Sgt3 genes than those of the remaining tested genes. Across the three red light illumination durations, Sgt3 showed high and stable expression, although it showed less stability across the different genotypes. Interestingly, the expression patterns of the seven genes were extremely similar for the 12 h and 24 h treatments. It was found that at least 6 h of red light illumination was required for optimal gene expression in all five genotypes, particularly in the genotype Zhuangshu-3 (DXY) after 24 h of treatment. Additionally, significant expression of the seven genes was observed in the L-6 genotype after 12 and 6 h of red light illumination. These results highlight that GGE biplots are an appropriate tool for analyzing and illustrating the differential expression profiles of the seven key genes involved in SGA biosynthesis in potato tubers. This study provides valuable insights into the biosynthesis and metabolism of SGAs in potatoes. Moreover, it demonstrates the potential application of GGE biplots in crop breeding and other research fields. Full article