Developing disease models to simulate and analyse yield losses for various pathogens is a challenge for the crop modelling community. In this study, we developed and tested a simple method to simulate septoria tritici blotch (STB) in the Cropsim-CERES Wheat model studying the impacts of damage on wheat (Triticum aestivum L.) yield. A model extension was developed by adding a pest damage module to the existing wheat model. The module simulates the impact of daily damage on photosynthesis and leaf area index. The approach was tested on a two-year dataset from Argentina with different wheat cultivars. The accuracy of the simulated yield and leaf area index (LAI) was improved to a great extent. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). In addition, a sensitivity analysis of different disease progress curves on leaf area index and yield was performed using a dataset from Germany. The sensitivity analysis demonstrated the ability of the model to reduce yield accurately in an exponential relationship with increasing infection levels (0–70%). The extended model is suitable for site specific simulations, coupled with for example, available remote sensing data on STB infection.
The determination of plant nitrogen (N) content (%) in wheat via destructive lab analysis is expensive and inadequate for precision farming applications. Vegetation indices (VI) based on spectral reflectance can be used to predict plant N content indirectly. For these VI, reflectance from space-borne, airborne, or ground-borne sensors is captured. Measurements are often taken at the canopy level for practical reasons. Hence, translocation processes of nutrients that take place within the plant might be ignored or measurements might be less accurate if nutrient deficiency symptoms occur on the older leaves. This study investigated the impact of leaf number and measurement position on the leaf itself on the determination of plant N content (%) via reflectance measurements. Two hydroponic experiments were carried out. In the first experiment, the N fertilizer amount and growth stage for the determination of N content was varied, while the second experiment focused on a secondary induction of N deficiency due to drought stress. For each plant, reflectance measurements were taken from three leaves (L1, L2, L3) and at three positions on the leaf (P1, P2, P3). In addition, the N content (%) of the whole plant was determined by chemical lab analysis. Reflectance spectrometer measurements (400–1650 nm) were used to calculate 16 VI for each combination of leaf and position. N content (%) was predicted using each VI for each leaf and each position. Significant lower mean residual error variance (MREV) was found for leaves L1 and L3 and for measurement position on P3 in the N trial, but the difference of MREV between the leaves was very low and therefore considered as not relevant. The drought stress trial also led to no significant differences in MREV between leaves and positions. Neither the position on the leaf nor the leaf number had an impact on the accuracy of plant nitrogen determination via spectral reflectance measurements, wherefore measurements taken at the canopy level seem to be a valid approach.
Multi-modeling (MM) approaches allow increasing modeling accuracy through a combination of different modeling structures for the simulation of plant growth and yield. The Decision Support System for Agrotechnology Transfer (DSSAT) 4.7 modeling platform currently includes three different wheat (Triticum aestivum L.) models (CERES, N-Wheat, and Cropsim). However, the main obstacle for using an MM approach is the calibration procedure. Calibration is time consuming and complex, especially if the user is not familiar with all three models. It results in a subjective calibration optimum and might discriminate models if the user is less trained. To avoid these conflicts, an automated calibration program which optimizes cultivar coefficients based on the root means square error (RMSE) of time-series data was developed to ensure objective calibration results across three different wheat models and to highlight the potential of MM approaches for decision support in the future. Model calibration was performed on a 4-yr nitrogen wheat fertilizer trial (0-240 kg ha −1 N) in southwest Germany. The evaluation mean showed satisfying results for the calibration (d-index = .93) and evaluation dataset (d-index = .81). By comparing different years, the MM approach improved modeling accuracy in most cases. Especially in the drought season of 2018, the MM approach revealed higher modeling accuracy for yield (d-index = .61) in contrast to a single simulation of CERES (d-index = .34) and Cropsim (d-index = .39). This demonstrated the advantage of an MM approach as different modeling structures could compensate for errors that occur in single modeling approaches.
Over the last decade, efforts to breed new Cannabis sativa L. cultivars with high Cannabidiol (CBD) and other non-psychoactive cannabinoids with low tetrahydrocannabinol (THC) levels have increased. In this context, the identification of the viability and quantity of pollen, which represents the fitness of male gametophytes, to accomplish successful pollination is of high importance. The present study aims to evaluate the potential of impedance flow cytometry (IFC) for the assessment of pollen viability (PV) and total number of pollen cells (TPC) in two phytocannabinoid-rich cannabis genotypes, KANADA (KAN) and A4 treated with two different chemical solutions, silver thiosulfate solution (STS) and gibberellic acid (GA3). Pollen was collected over a period of 8 to 24 days after flowering (DAF) in a greenhouse experiment. Impedance flow cytometry (IFC) technology was used with Cannabis sativa to assess the viability and quantity of pollen. The results showed that the number of flowers per plant was highest at 24 DAF for both genotypes, A4 (317.78) and KAN (189.74). TPC induced by STS was significantly higher compared to GA3 over the collection period of 8 to 24 DAF with the highest mean TPC of 1.54 × 105 at 14 DAF. STS showed significantly higher viability of pollen compared to GA3 in genotype KAN, with the highest PV of 78.18% 11 DAF. Genotype A4 also showed significantly higher PV with STS at 8 (45.66%), 14 (77.88%), 18 (79.37%), and 24 (51.92%) DAF compared to GA3. Furthermore, counting the numbers of flowers did not provide insights into the quality and quantity of pollen; the results showed that PV was highest at 18 DAF with A4; however, the number of flowers per plant was 150.33 at 18 DAF and was thus not the maximum of produced flowers within the experiment. IFC technology successfully estimated the TPC and differentiated between viable and non-viable cells over a period of 8 to 24 DAF in tested genotypes of Cannabis sativa. IFC seems to be an efficient and reliable method to estimate PV, opening new chances for plant breeding and plant production processes in cannabis.
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