Modelling Daily Surface Runoff, Sediment and Nutrient Loss at Watershed Scale Employing APEX Model Interfaced with GIS – A Case Study in Himalayan Landscape

Daily surface runoff, sediment and nutrient loss data collected from a watershed located in Uttarakhand state of Indian Himalayan region, in year 2010-2011 and of which half of the events data were used for calibration and remaining for validation. Model was calibrated for surface runoff, sediment loss and nutrient loss to optimize the input given to the model to predict the sediment loss, erosion and nutrient loss. The calibration was done by changing the sensitive parameters. Analysis showed that SCS CN number was found most sensitive to runoff, followed by saturated hydraulic conductivity, available water-holding capacity, CN retention parameter and C factor whereas erosion control practice (P) factor was found to be most sensitive, followed by C factor, sediment routing coefficient, average upland slope and soil erodibility (K) factor for the sediment and nutrient loss. APEX model calibrated for the watershed and it predicted quite well for the surface runoff (r=0.92, NSE=0.50), sediment loss (r=0.88, NSE=0.61 and nutrients of total carbon (r=0.78, NSE=0.59) and fairly for total nitrogen (r=0.77, NSE=0.19). Surface runoff was predicted well for low and medium rainfall; however, it was over predicted for high rainfall events. Over prediction may be attributed to the unaccountable conservation measures and practices which were not accounted by the model. Similarly, sediment loss was estimated on daily basis at the watershed scale and was well predicted for low and medium rainfalls but under-estimated for high rainfall events. The area is prone to landslips occurred at high rainfall events was not accounted by the model that may be a reason for under prediction of sediment loss by the model.

Can a chemical be both readily biodegradable AND very persistent (vP)? Weight-of-evidence determination demonstrates that phenanthrene is not persistent in the environment

Under the European REACH regulation, chemicals are assessed for persistence as part of weight-of-evidence determinations of persistence, bioaccumulation and toxicity (PBT), as required under Annex XIII and supported by an Integrated Assessment and Testing Strategy (ITS). This study describes the persistence assessment of phenanthrene, a data-rich polycyclic aromatic hydrocarbon (PAH), in accordance with this framework. All available data from screening and simulation tests, for water, soil and sediment compartments, plus other relevant information, have been compiled. These have been evaluated for reliability and relevance, and a weight-of-evidence determination of persistence has been carried out. Aspects relevant to the assessment, such as degradation metabolites, non-extractable residues (NER), test temperature and bioavailability, have also been considered. The resulting assessment considered a wide range of evidence, including 101 experimental data points. Phenanthrene was demonstrated to be readily biodegradable, a first-tier screen for non-persistence in the ITS. Furthermore, weight-of-evidence assessment of data for water, soil and sediment compartments supported a conclusion of “not persistent” (not P). In non-standard soil studies with sludge-amended soils, longer half-lives were observed. This was attributable to pyrogenic sources of and significantly reduced bioavailability of phenanthrene, highlighting the importance of bioavailability as a major source of variability in persistence data. Available simulation test data for the sediment compartment were found to be unreliable due to the anoxic impact of the use of a biodegradable solvent in a closed system, and were inconsistent with the broader weight of evidence. Estimation of photodegradation using AOPWIN and the APEX model demonstrated this to be an important fate process not currently considered in persistence assessments under REACH. The assessment is not in agreement with a recent regulatory decision in which phenanthrene was determined to be very persistent (vP). This assessment provides a case study for persistence assessment using the REACH ITS and highlights the need for improved guidance to improve consistency and predictability of assessments. This is particularly important for complex cases with data-rich chemicals, such as phenanthrene.

Simulating Soybean–Rice Rotation and Irrigation Strategies in Arkansas, USA Using APEX

With population growth and resource depletion, maximizing the efficiency of soybean (Glycine max [L.] Merr.) and rice (Oryza sativa L.) cropping systems is urgently needed. The goal of this study was to shed light on precise irrigation amounts and optimal agronomic practices via simulating rice–rice and soybean–rice crop rotations in the Agricultural Policy/Environmental eXtender (APEX) model. The APEX model was calibrated using observations from five fields under soybean–rice rotation in Arkansas from 2017 to 2019 and remote sensing leaf area index (LAI) values to assess modeled vegetation growth. Different irrigation practices were assessed, including conventional flooding (CVF), known as cascade, multiple inlet rice irrigation with polypipe (MIRI), and furrow irrigation (FIR). The amount of water used differed between fields, following each field’s measured or estimated input. Moreover, fields were managed with either continuous flooding (CF) or alternate wetting and drying (AWD) irrigation. Two 20-year scenarios were simulated to test yield changes: (1) between rice–rice and soybean–rice rotation and (2) under reduced irrigation amounts. After calibration with crop yield and LAI, the modeled LAI correlated to the observations with R2 values greater than 0.66, and the percent bias (PBIAS) values were within 32%. The PBIAS and percent difference for modeled versus observed yield were within 2.5% for rice and 15% for soybean. Contrary to expectation, the rice–rice and soybean–rice rotation yields were not statistically significant. The results of the reduced irrigation scenario differed by field, but reducing irrigation beyond 20% from the original amount input by the farmers significantly reduced yields in all fields, except for one field that was over-irrigated.

Adapting the APEX Model to Simulate Detasseling in Inbred Corn for Hybrid Seed Production

HighlightsThe APEX model was adapted to simulate detasseling in inbred corn for hybrid seed production.The adapted model satisfactorily predicted detasseling effects on LAI, grain yield, and N content.An inbred corn model could be applied to evaluate best management practices for inbred corns.Abstract. Hybrid seed corn production comprises approximately 10% of the entire corn acreage in the U.S. Because of seed corn’s high economic value, and to maximize yields, seed corn growers often over-irrigate or apply nitrogen (N) fertilizers equal to or in excess of those recommended for commercial hybrid corn. Detasseling female corn inbred lines during hybrid corn seed production is critical to ensure the purity of seeds. In addition to the removal of tassels, detasseling also results in the removal of several leaves, which may lead to reduced seed yields. The objective of this study was to adapt the Agricultural Policy/Environmental eXtender (APEX) model to simulate the detasseling of female inbred corns in hybrid seed production. An APEX inbred corn model was developed to simulate the effects of detasseling and leaf removals on the development of inbred corn, leaf area index (LAI), grain yield, and grain N content. Growth characteristics of inbred corn were parameterized in APEX using data from a field study conducted in Nebraska. Overall, the APEX inbred corn model successfully predicted the effects of detasseling on LAI, grain yield, and grain N content under the conditions of the field experiment. There was a significant correlation between simulated and measured LAI (Pearson r = 0.86 and R2 = 0.74 at p = 0.05). The computed paired t-test and permutation test p-values indicated no significant differences between measured and simulated LAI. The mean simulation percent difference and percent bias (PBIAS) were respectively 4.2% and 4.7%, while measured and simulated LAI values had an average root mean square error (RMSE) of 0.14. The APEX model predicted grain yield with RMSE of 120 kg ha-1, mean simulation percent difference of 0.48%, and PBIAS of 0.26%. Like LAI, predicted grain yields exhibited significant correlation with field data (Pearson r = 0.99 and R2 = 0.97 at p = 0.05). Similarly, computed paired t-test and permutation test p-values indicated no significant differences between measured and simulated grain yields. Grain N content was predicted with RMSE of 6.75 kg N ha-1, mean simulation percent difference of 1.46%, and PBIAS of 2.45%. Predicted and measured grain N content values were correlated (Pearson r = 0.81 and R2 = 0.65 at p = 0.05), while the t-test and permutation test p-values indicated no significant differences between measured and predicted grain N content. Overall, detasseling effects were better predicted for grain yield than for LAI and grain N content as indicated by a Nash-Sutcliffe efficiency (NSE) of 0.92 compared to NSE values of 0.47 for LAI and 0.43 for grain N content. In conclusion, the hybrid seed corn industry could benefit from the application of inbred corn models that could allow growers to evaluate and identify optimal irrigation and N management practices for inbred corn, similar to the benefits that have been obtained with model simulation for commercial hybrid corn grain production systems. Keywords: APEX parameterization, Detasseling, Inbred corn, Leaf area index.