Jianqiang He

Applying glue for estimating CERES-Maize genetic and soil parameters for sweet corn production.

Sweet corn (Zea mays L.) is one of the five most valuable vegetable crops in Florida. The application of nitrogen fertilizer is necessary for farmers to reliably produce sweet corn. The use of crop simulation models can facilitate the evaluation of management practices that are profitable with minimal unwanted impacts on the environment. Before using such models in decision making, it is necessary to specify model parameters and understand the uncertainties associated with simulating variables that are needed for decision making. The generalized likelihood uncertainty estimation (GLUE) method was used to estimate genotype and soil parameters of the CERES-Maize model of the Decision Support System for Agrotechnology Transfer (DSSAT). The uncertainties in predictions for sweet corn production in northern Florida were evaluated using the existing field corn genotype coefficient and soil parameter database contained within DSSAT and field data collected during a series of experiments carried out in 2005 and 2006. Genotype coefficients (P1, P5, and PHINT) and soil parameters (SLDR, SLRO, SDUL, SLLL, and SSAT) were generated using a multivariate normal distribution that preserved the correlations between parameters. The soil parameter SLPF was not correlated with other parameters and was generated with a uniform distribution. After parameters were estimated, the CERES-Maize model correctly predicted the dry matter yields, anthesis dates, and harvest dates. The mean values of these variables were close to those measured in the field, with an average relative error of 4.4% and 2.4% for the data sets of 2005 and 2006, respectively. The calibrated CERES-Maize model simulated the temporal trend of leaf TKN concentration accurately during the early stage of the growth season, but underestimated the leaf TKN concentrations during the latter half of the season. The GLUE procedure accurately estimated soil parameters (SLLL, SDUL, and SSAT) when compared to independent measurements made in the laboratory, with an average absolute relative error of about 8.5%. The simulated time series of soil water content adequately simulated the observed soil water changes during both growth seasons for every layer. However, there were some large differences between simulated and observed soil nitrate contents. In a relevant further study, the average absolute relative error between model-predicted and field-estimated amounts of potential nitrogen leaching was 15.3%, which is much better than some reported comparable studies of nitrogen leaching modeling. In the posterior distribution of estimated parameters, the uncertainties in parameters were substantially reduced, with CV values mostly lower than 10%. The average CV value of the parameters was reduced from 27.2% in the prior distribution to 4.6% in the posterior distribution. In general, the results of this study showed that the CERES-Maize model was capable of simulating sweet corn production in northern Florida and the associated soil water content. The model can also simulate potential nitrogen leaching with acceptable accuracy. We suggest that the model can now be used to compare different management practices relative to productivity and potential nitrogen leaching outcomes.

In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management

Highlight A global uncertainty and sensitivity analysis of the process-based model SiriusQuality2 was carried out to quantify the relationship between simple morpho-physiological traits and grain yield and protein concentration for wheat.

EVALUATION OF SWEET CORN YIELD AND NITROGEN LEACHING WITH CERES-MAIZE CONSIDERING INPUT PARAMETER UNCERTAINTIES

A study was conducted to evaluate the ability of the CERES-Maize model in the Decision Support System for Agrotechnology Transfer (DSSAT) to simulate sweet corn (Zea mays L. var. saccharata) yield and nitrogen leaching in Florida, considering input parameter uncertainties. In this type of biological system modeling, uncertainties in predictions with respect to input parameter uncertainty are often not reported. Thus, the result of model verification could be misleading if there are large uncertainties in field observations, since single model prediction values cannot comprehensively represent heterogeneous field conditions. Instead, comparisons between the distributions of model simulations and field observations were recommended in this study. A two-factor split-plot field experiment was conducted with three nitrogen fertilizer levels (185, 247, and 309 kg N ha-1) and two irrigation levels (I1 and I2; I2 = 1.5 × I1, where I1 is the irrigation demand calculated based on a daily soil water balance). Yield response to different nitrogen fertilizer and irrigation management levels was evaluated, and the cumulative nitrogen leaching was estimated for each of the treatments based on a nitrogen balance. Next, the field experiment treatments were simulated with the calibrated CERES-Maize model using parameter sets generated from parameter distributions derived with the generalized likelihood uncertainty estimation (GLUE) method in a previous study. Simulated dry matter yields and cumulative nitrogen leaching were compared to field-measured or estimated values. Measured total and marketable yields were not affected by irrigation level. Estimated nitrogen leaching increased significantly with higher levels of irrigation and nitrogen fertilizer application. The calibrated CERES-Maize model accurately predicted the phenology dates, with an error of 0 and 1 day for anthesis and maturity dates, respectively. The prediction uncertainties (due to uncertain input parameter values), as measured by the standard deviation (SD) in predicted anthesis and maturity dates, were only 1 and 2 days after planting, respectively. The model also accurately predicted the changes in dry matter yield caused by different nitrogen and irrigation levels, with a relative absolute error (RAE) less than 12% for all but one treatment. Due to the uncertainties in soil and genetic parameters, the prediction SD of simulated dry yields ranged from 655 kg ha-1 at I1 to 960 kg ha-1 at I2, while the observation SD ranged from 220 to 463 kg ha-1 for measured dry yields. The uncertainties in simulated dry yield were higher than the uncertainties of measured values due to relatively high variations in estimated genetic coefficients. The model performance could be improved further if the variations in estimated genetic coefficients could be reduced. The difference between the simulated and estimated nitrogen leaching amounts was significant and complex, ranging from -31 to 43 kg N ha-1 with an average absolute difference of 15.3%. This discrepancy was probably due to both the errors in estimation of potential nitrogen leaching in the field experiment using a mass balance approach and the inaccuracy of model predictions. Nevertheless, the increase in nitrogen leaching resulting from higher nitrogen fertilizer levels was correctly predicted. The uncertainties in simulated N leaching covered more than 67% of the uncertainties of estimated leaching for all but one treatment, indicating that estimated soil parameters via the GLUE method were able to represent the heterogeneity of field soil. In general, the CERES-Maize model is able to simulate sweet corn production under different management conditions sufficiently to allow exploration of tradeoffs between crop yield and nitrogen leaching for sweet corn production in Florida.

Uncertainties in Simulating Crop Performance in Degraded Soils and Low Input Production Systems

Many factors interact to determine crop production. Cropping systems have evolved or been developed to achieve high yields, relying on practices that eliminate or minimize yield reducing factors. However, this is not entirely the case in many developing countries where subsistence farming is common. The soils in these countries are mainly coarse-textured, have low water holding capacity, and are low in fertility or fertility declines rapidly with time. Apart from poor soils, there is considerable annual variability in climate, and weeds, insects and diseases may damage the crop considerably. In such conditions, the gap between actual and potential yield is very large. These complexities make it difficult to use cropping system models, due not only to the many inputs needed for factors that may interact to reduce yield, but also to the uncertainty in measuring or estimating those inputs. To determine which input uncertainties (weather, crop or soil) dominate model output, we conducted a global sensitivity analysis using the DSSAT cropping system model in three contrasting production situations, varying in environments and management conditions from irrigated high nutrient inputs (Florida, USA) to rainfed crops with manure application (Damari, Niger) or with no nutrient inputs (Wa, Ghana). Sensitivities to uncertainties in cultivar parameters accounted for about 90% of yield variability under the intensive management system in Florida, whereas soil water and nutrient parameters dominated uncertainties in simulated yields in Niger and Ghana, respectively. Results showed that yield sensitivities to soil parameters dominated those for cultivar parameters in degraded soils and low input cropping systems. These results provide strong evidence that cropping system models can be used for studying crop performance under a wide range of conditions. But our results also show that the use of models under low-input, degraded soil conditions requires accurate determination of soil parameters for reliable yield predictions.

Detection of Cavitation in a Venturi Injector With a Combined Method of Strain Gauges and Numerical Simulation

The fertilizer suction capability of a Venturi injector is dependent on the vacuum pressure in the throat portion. As the vacuum level drops below the saturation vapor pressure, the pressure decreases to a particular value corresponding to the maximum pressure difference (Δpmax) between inlet and outlet pressures, and critical cavitation is likely to occur, leading to an unstable suction flow rate and low fertilization uniformity. A new method of using strain gauges to detect cavitation in Venturi injectors was explored experimentally and verified numerically under various operating conditions. The standard deviation (SD) of the measured strain values and the simulated values of the vapor-phase volume fraction (Vf) were used to evaluate the influence of cavitation. The results showed that both the rate of increase (ηm) of the average SD and the average growth rate (AGR) of the simulated cavitation length reach relatively large values at the maximum pressure difference (Δpmax), where the measured suction flow rate simultaneously reaches a maximum. In addition, SD and Vf shared similar variation trends at pressure differences larger than the corresponding Δpmax under various conditions. This new cavitation detection method has been proved to be feasible and reliable. It helps to determine accurately the value of Δpmax at different inlet pressures and to ensure that the Venturi injector runs in a safe operating-pressure range.

GGE biplot analysis of genetic variations of 26 potato genotypes in semi-arid regions of Northwest China

The purpose of this study was to evaluate the yield performance of 26 potato (Solanum tuberosum L.) genotypes in seven test environments cross the semi-arid potato growing region of Northwest China. The tested potato genotypes were planted in a randomized complete block design with three replicates in two growth seasons (2007–08, 2009–10). The graphical tool of GGE (genotype main effect [G] and genotype and environment interaction [GE]) biplot was applied to analyse the multi-environment trials data obtained. The results indicate that the test sites could be grouped into one mega-environment; the best performing and candidate genotypes for the region were identified. Among seven test environments, one test site has the greatest discriminating ability where two sites can be dismissed from future trials due to the similarity of their ability of discrimination and representation to other sites. The results reveal that the GGE biplot is useful in identifying potato genotypes with yield and stability performance in semi-arid regions of Northwest China.