Yubin Yang

Accuracy of numerical methods for solving the advection–diffusion equation as applied to spore and insect dispersal

Abstract Three algorithms for solving a simplified 3-D advection–diffusion equation were compared as to their accuracy and speed in the context of insect and spore dispersal. The algorithms tested were the explicit central difference (ECD) method, the implicit Crank–Nicholson (ICN) method, and the implicit Chapeau function (ICF) method. The three algorithms were used only to simulate the diffusion process. A hold-release wind shifting method was developed to simulate the wind advection process, which shifts the concentration an integer number of grids and accumulates the remaining wind travel distance (which is less than the grid spacing) to the next time step. The test problem was the dispersal of a cloud of particles (originally in only one grid cell) in a 3-D space. The major criterion for testing the accuracy was R 2 , which represents the proportion of the total variation in particle distribution in all grid cells that is accounted for by the particle distribution through numerical solutions. Other criteria included total remaining mass, peak positive density, and largest negative density. High R 2 values were obtained for the ECD method with (Δ t K z )/(Δ z ) 2 ≤0.5 (Δ t =time step; K z =vertical eddy diffusion coefficient; Δ z =vertical grid spacing), and for the two implicit methods with Δ t K z /(Δ z ) 2 ≤5. The ICN method gave higher R 2 values than the ICF method when the concentration gradients were high, but its accuracy decreased more rapidly with the progress of time than the ICF method with a combination of a large grid spacing and a large time step. With very steep concentration gradients, the ICF method generated huge negative values, the ICN method generated negative values to a lesser extent, and the ECD method generated only small negative values. It was also found that good mass and/or peak preservation did not necessarily correspond to a higher R 2 value. Based on the R 2 value and the requirement for concentration positivity, for simulations with steep concentration gradients, the ECD method would be most appropriate, followed by the ICN method, and the ICF method would be least appropriate due to large negative values. For simulations with low concentration gradients, the ECD or ICF or ICN method could be used, but the ICN method would not be appropriate for use in a combination of a large time step and a large grid spacing. The results from this study could help selection and use of appropriate numerical methods in studying the spatial dynamics of spores and insects.

A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance. A taxonomy-based approach was used to classify AgMIP rice simulation models.Different model structures often resulted in similar outputs.Similar structures often led to large differences in outputs.User subjectivity likely hides relationships between model structure and behaviour.Shared protocols are still needed to limit the risks during calibration.

Feasibility of Automatic Aeration for Insect Pest Management for Rice Stored in East Texas

Aeration using automatic controllers was compared with manually-activated aeration (manual aeration) in bins of farm-stored rice in Nome, Texas, from 17 September 2002 through the end of the year. Manual aeration was defined as the farm owner manually activating the fans in mid-October, while automatic aeration employed activation temperatures of 23.9°C, 15.6°C, and 7.2°C for three discreet cooling cycles. Population development of Rhyzopertha dominica (F.), the lesser grain borer, and Sitophilus oryzae (L.), the rice weevil, was assessed by confining 20 adults of each species with 150 g of rough rice in separate cages placed at 6 different locations in the top of the rice mass. Total heat units at temperatures above 15°C were 150 to 300 degree days (DD) lower in bins with automatic aeration compared to manual aeration. Temperatures from 17 September through mid-October were 8°C to 10°C less in bins with automatic aeration than in with manual aeration, and 3°C to 6°C less during the remainder of the year. The number of adult R. dominica in the cages from bins with manual aeration were 45.4 ± 13.1, 114.5 ± 17.7, and 223.0 ± 24.8 after 5, 10, and 15 weeks, respectively, while populations in cages from bins with automatic aeration were significantly less (P < 0 .05); 0.8 ± 0.3, 24.5 ± 4.5, and 21.7 ± 2.7 after 5, 10, and 15 weeks, respectively. There was no statistical difference (P = 0.05) in the number of adult S. oryzae collected in cages from bins with manual versus controlled aeration after 5 weeks (11.7 ± 8.1 and 0.3 ± 0.3, respectively), 10 weeks (14.7 ± 7.1 and 18.0 ± 9.6, respectively), and 15 weeks (39.0 ± 21.2 and 10.5 ± 5.6, respectively). However, the variation in the data set could have masked the apparent differences in the two aeration strategies.

Use of a Web-Based Model for Aeration Management in Stored Rough Rice

ABSTRACT A web-based model was used to simulate the impact of aeration on population growth of the lesser grain borer, Rhyzopertha dominica (F.), and the rice weevil, Sitophilus oryzae (L.), in stored rough rice, Oryza sativa L., at Beaumont, TX. Simulations were run for each of 10 yr with 1 August as the start date; 31 December as the end date; beginning populations of 2.5 adults per metric ton (1,000 kg); starting grain temperatures of 29.4, 32.2, and 35.0°C; and aeration airflow rates of 0.27, 0.79, and 1.40 m3/min/metric ton of rice. In the absence of aeration, populations of both species increased exponentially, with maximum production of R. dominica and S. oryzae at starting grain temperatures of 35.0 and 32.2°C, respectively. Final predicted populations of R. dominica on 31 December from grain starting temperatures of 29.4, 32.2, and 35.0°C were 5,465, 6,848, and 11,855 per ton, respectively; final predicted populations of S. oryzae were 13,288, 21,252 and 4,355, respectively. Aeration led to a reduction in grain temperature and a decrease in pest populations, regardless of starting grain temperature or aeration airflow rates. Predicted populations of R. dominica on 31 December ranged from 12 to 63 adults per ton at all grain starting temperatures and airflow rates; populations of S. oryzae on 31 December ranged from 108 to 193 adults per ton at all grain starting temperatures and airflow rates. The predicted population levels in aerated rice represented at least a 98% reduction compared with unaerated rice. Results show the utility of the web-based model and how the various model inputs can help define broader patterns of insect control in rice stored in the south central United States.

Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments

The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.