A dynamic model forecasting infection of pear leaves by conidia of <Emphasis Type="Italic">Venturia nashicola</Emphasis> and its evaluation in unsprayed orchards

A dynamic model, called VenInf, was developed to forecast infection of pear leaves by conidia of Venturia nashicola. By simulating conidial infection processes following a rain event, the model estimates % conidia that successfully infected leaves at the end of an infection period. The model is mainly derived from logistic models developed from recent laboratory and glasshouse experimental results on infection of pear seedlings to estimate the rates of infection and mortality. It simulates the conidial infection process at 5 min intervals using temperature, relative humidity (RH), surface wetness and rainfall as input. The model was evaluated against pear scab in four unsprayed orchards in China over a 4-year period. In all orchards, all significant disease increases were associated with infection periods predicted by the model. In one orchard, in 2004 the incidence of leaf infection remained very low (<3%) during the entire season despite the model forecasting several severe infection periods. Results of orchard evaluation suggest that the model is able to identify all important potential infection periods. Thus, further field studies should be carried out to determine whether and how the model can be used in practice to assist farmers in making decisions on fungicide applications.     

Population dynamics of Rumex obtusifolius under contrasting lucerne cropping systems

The population dynamics of Rumex obtusifolius L. was analysed in a lucerne:winter cereal crop rotation by means of a matrix population model that takes into consideration two crop rotation periods: the lucerne (Medicago sativa L.) cropping period and the cereal cropping period. Several transition matrices based on life-cycle stages were calculated for each cropping period using experimental data and were used in the construction of a model that analyses the population dynamics of R. obtusifolius under different harvest dates and lengths of lucerne cropping periods. Model projections showed that populations of R. obtusifolius increased during the lucerne cropping period regardless of harvest date and decreased during the cereal cropping period. Under a late harvest date, populations decreased at each crop rotation when lucerne was grown for 3 years, remained close to the equilibrium when lucerne was left to grow for 5 years, and increased for longer lucerne cropping periods. In contrast, populations of R. obtusifolius decreased even with a lucerne cropping period of 9 years under an early harvest date. The significance of these results in relation to the biology and the non-chemical control of the species is discussed.     

Predicting Avena spp. control with clodinafop

Previous analyses of two independent data sets, one generated by industry and the other involving purpose-designed field experiments, showed that the factors relating to Avena spp. control with clodinafop in Australia are fairly consistent. This article details the combination of those, together with additional new industry data, into an overall set that was subject to linear mixed model and covariate analyses for the purpose of developing a predictive model. Cross-validation methods were used to assess the potential for agronomic and environmental variables at the time of spraying clodinafop to predict Avena spp. mortality. The analyses showed that clodinafop dose, available soil moisture, cumulative minimum temperatures, maximum temperature on the day of spraying, spray water volume and the spray water volume by maximum temperature interaction at spraying were useful predictors and these were subsequently incorporated into a model. This model allows growers and agronomists to use knowledge of weather conditions on the day of application to tailor clodinafop dose and water volume accordingly, or to avoid spraying if they are adverse. The model's potential to improve herbicide efficiency and be used as part of a long-term Avena spp. management programme are briefly discussed.     

Development of <Emphasis Type="Italic">Peronospora parasitica</Emphasis> epidemics on radish as modelled by the effects of water vapour saturation deficit and temperature

Epidemics of Peronospora parasitica are strongly affected by temperature and air moisture, and the interaction of these factors. Because a significant percentage of radish plants are grown in greenhouses, it may be possible to influence epidemics by altering the greenhouse climate. The objective of this study was to test the hypothesis that epidemics of P. parasitica can be modelled by the effects of air temperature and moisture in the greenhouse. Such a model could then be used to analyse greenhouse climate control strategies with regard to managing downy mildew. Five radish crops were grown under greenhouse conditions with set-points for heating and ventilation intended to obtain favourable conditions for disease development during the first part of the growing cycle. Subsequent to this first phase, unfavourable conditions were set until harvest. Disease incidence was measured once a week until the radishes reached marketable size. In addition, experiments were carried out in growth chambers in which inoculated plants were subjected to air temperatures between 8 and 27°C, and disease incidence and sporulation intensity were measured. Data from these two experiments were then used to estimate model parameters. In this model, the interactions of air temperature (T) and water vapour saturation deficit (SD) were adequately described by a multiplicative relationship. The simulated epidemics by the fitted model were highly correlated with the observed epidemics (r = 0.91, R ² = 0.83, n = 29). Parameter estimates indicated that T of ca. 20°C and SD < 0.03 hPa resulted in the highest rates of disease development and that the rate was zero when SD > 2.0 hPa. Both experimental data and simulations showed that epidemics of P. parasitica can be effectively controlled by managing the greenhouse climate.     

Temperature-dependent development rates of <Emphasis Type="Italic">Bracon vulgaris,</Emphasis> a parasitoid of boll weevil

The duration of development of Bracon vulgaris Ashmead, parasitoid of the boll weevil Anthonomus grandis Boheman, was determined at nine constant temperatures between 18°C and 38°C. Nonlinear regression analysis was used to test the fit of temperature-dependent development rates to the Sharpe and DeMichele and Lactin et al. models. At the highest tested temperature (38°C) all the parasitoid eggs died before hatching and no evidence of development was observed. The high values of R ² for the models of Sharpe and DeMichele (0.8432 to 0.9834), and Lactin et al. (0.9071 to 0.9795) indicated that these models are suitable to estimate the development rate of B. vulgaris as a function of temperature. B. vulgaris showed tolerance to high temperature which is represented by the high value of H _H (change in enthalpy associated with high-temperature inactivation of the enzyme) for the prepupa stage of this insect obtained with the Sharpe and DeMichele model. According to that model, B. vulgaris exhibits thermal stress at 35.7°C, which indicates that maximum thermal stress estimated by this model was close to the real one.     

Economic threshold for the management of <Emphasis Type="Italic">Plutella xylostella</Emphasis> with granulovirus in cauliflower ecosystem

Resistance to chemicals and some biological control agents by Plutella xylostella has resulted in the search for alternatives, such as P. xylostella granulovirus (PlxyGV), for the management of P. xylostella. However, use of PlxyGV would be economical only if it is applied at the right crop stage and at economic threshold levels. Hence, we evaluated the field level economic threshold for application of PlxyGV at the reproductive phase of cauliflower taking into consideration both the economic threshold model and the economic optimization model. PlxyGV was found to be very effective in management of the pest and was comparable to weekly applications of quinalphos 25EC. However, due to the higher cost of PlxyGV application than quinalphos, the cost benefit ratio with quinalphos application was much higher than the virus application. The net income from the virus application decreased both at lower thresholds—due to the higher cost of plant protection, and at higher thresholds—due to the increased damage by the pest. Based on the economic threshold model, the economic threshold for application of PlxyGV was estimated at 9–11 larvae per ten plants. Considering the net income per hectare and the cost of plant protection, it was found that at a threshold of 12–14 larvae per ten plants net income was just equal to the cost of plant protection. Based on the economic optimization model, the optimum threshold for application of PlxyGV was 15 larvae per ten plants when profits are maximum.     

Structure–Activity Relationships of Herbicidal Aryltriazolinones

A series of substituted aryltriazolinones, known to inhibit protoporphyrinogen oxidase, were prepared and their structure–activity requirements at positions 4 and 5 of the aromatic ring investigated. A QSAR equation obtained for substituents at the 5 position identified the hydrophobicity term π and the Sterimol minimum width B₁ as the two parameters affecting in-vitro biological activity. Greenhouse pre-emergence activity correlated with in-vitro activity and the hydrophobicity term π of the substituent at that position. It was found that the phenoxy-4-oxyacetate group at aromatic position 5 was an outlier and had to be considered separately. SAR analysis of substituents at aromatic position 4 revealed that two different models were required to explain all observed substituent effects. In the first model, where the 5 position was occupied by hydrogen, the 4-chlorobenzyloxy group at aromatic position 4 gave the best compound. The second model, where the 5 position of the aromatic ring was occupied by a group other than hydrogen, resulted in a QSAR equation, previously derived, which links substituent effects at position 4 with π and with the electronic para inductive term F_p. In this model the chloro group provides optimum biological activity. The need to separate the aryltriazolinone herbicides into several different classes in order to explain their substituent effects at aromatic positions 4 and 5 could be rationalized if more than one binding conformation, within the same binding site, is possible. © 1997 SCI     

A comparative molecular field analysis study of obtusifoliol 14α-methyl demethylase inhibitors

This report describes the development of a Comparative Molecular Field Analysis (CoMFA) model from a set of obtusifoliol 14α-methyl demethylase (DM) inhibitors to aid in the design of herbicides targeting sterol biosynthesis. CoMFA is a three-dimensional (3-D) quantitative structure–activity relationship (QSAR) method that is useful in the probing of receptor binding sites when experimental structure data are unavailable. Conformational analysis and SAR of some rigid and active analogs were used to build the initial model using the active analog hypothesis. The model was subsequently used to design compounds that retain the active site shape requirements, but incorporate physical properties that favor soil-applied herbicidal action. In addition, a second-generation CoMFA model incorporating the newly designed inhibitors was developed and represents the current understanding of the DM binding site. This model was derived from a pharmacophore developed from two methods, the active analog approach as well as from the Catalyst program. The fact that two independent methods produced a similar pharmacophore strengthens the validity of the model. © 1999 Society of Chemical Industry     

An evaluation of four crop : weed competition models using a common data set

Summary To date, several crop : weed competition models have been developed. Developers of the various models were invited to compare model performance using a common data set. The data set consisted of wheat and Lolium rigidum grown in monoculture and mixtures under dryland and irrigated conditions. Results from four crop : weed competition models are presented: almanac , apsim , cropsim and intercom . For all models, deviations between observed and predicted values for monoculture wheat were only slightly lower than for wheat grown in competition with L. rigidum , even though the workshop participants had access to monoculture data while parameterizing models. Much of the error in simulating competition outcome was associated with difficulties in accurately simulating growth of individual species. Relatively simple competition algorithms were capable of accounting for the majority of the competition response. Increasing model complexity did not appear to dramatically improve model accuracy. Comparison of specific competition processes, such as radiation interception, was very difficult since the effects of these processes within each model could not be isolated. Algorithms for competition processes need to be modularised in such a way that exchange, evaluation and comparison across models is facilitated.     

A Field Test of Root Zone Water Quality Model—Pesticide and Bromide Behavior

The Root Zone Water Quality Model (RZWQM) is a process-based model developed recently by USDA–ARS scientists. The model integrates physical, chemical and biological processes to simulate the fate and movement of water and agrochemicals over and through the root zone at a representative point in a field with various management practices. The model was evaluated using field data for the movement of water and bromide, and the transformation and transport of cyanazine and metribuzin in the soil profile. The model reasonably simulated soil water and bromide movement. Pesticide persistence was predicted reasonably well using a two-site sorption model that assumes a rate-limited (i.e. long-term) adsorption–desorption process with the additional assumption of negligible degradation of inter-aggregate adsorbed pesticides.     

Co‐operative versus non‐co‐operative farmers' weed control decisions in an agricultural landscape

This study presents a simple landscape model of the influence of seed dispersal on weed population dynamics between fields. In the model, three fields are interconnected through weed seed dispersal, where seed might move with field equipment, in irrigation water or may be wind‐dispersed. The model is intended to characterise the impact of field‐level weed management decisions on landscape‐level weed population dynamics. Two simple scenarios were studied. The first simulates farmers adopting common effective methods of control on each of the three fields. In the second scenario, farmers manage the weed population independently on each of the three fields. In the first scenario, weed populations were driven to extinction as might be expected with uniformly high levels of weed suppression in each of the three fields. In the second scenario, when the two nearest fields in the sequence experienced control, the weed population was driven to extinction in the second field but not in the first where weed populations were able to survive in spite of high levels of suppression. The results suggest that control measures within a field may not adequately reflect their impact on weed population dynamics when between field seed movement occurs. Another important result is the importance of proximity and spatial arrangement of fields and the resulting influence on weed population dynamics within a field.     

Long-term management of Striga hermonthica: strategy evaluation with a spatio-temporal population model

The parasitic weed Striga hermonthica poses a serious threat to cereal production in sub-Saharan Africa. Striga hermonthica seedbanks are long-lived; therefore, long-term effects of control strategies on the seedbank only emerge after several years. We developed a spatially explicit, stochastic model to study the effectiveness of control strategies in preventing invasion of S. hermonthica into previously uninfested fields and in reducing established infestations. Spatial expansion of S. hermonthica and decrease in millet yield in a field was slower, on average, when stochasticity of attachment of seedlings to the host was included and compared to the deterministic model. The spatial patterns of emerged S. hermonthica plants 4–7 years after point inoculation (e.g. seeds in a dung patch) in the spatial-stochastic model resembled the distribution typically observed in farmers' fields. Sensitivity analysis showed that only three out of eight life cycle parameters were of minor importance for seedbank dynamics and millet yield. Weeding and intercropping millet with sesame or cowpea reduced the seedbank in the long term, but rotations of millet with trap crops did not. High seedbank replenishment during years of millet monoculture was not sufficiently offset by seedbank depletion in years of trap crop cultivation. Insight from simulations can be employed in a participatory learning context with farmers to have an impact on S. hermonthica control in practice.     

‘Universal’ spray droplet adhesion model – accounting for hairy leaves

The adhesion of a spray droplet upon initial contact with a leaf surface is extremely important to spray efficacy and is dependent on dynamic interactions between droplets (formulation, size, velocity) and leaf (micro‐topography, surface chemistry, veininess, hairiness and orientation). A ‘universal’ spray droplet adhesion model has previously been developed, using 50% aqueous acetone contact angles as a measure of leaf surface properties; this model satisfactorily predicts initial adhesion over a range of formulation surface tensions, droplet sizes and velocities. However, it failed to fit data from hairier leaves. This study investigates initial spray droplet adhesion on hairy leaves. Two categories of hairy leaves were identified by how the droplets penetrate the leaf hairs, Wenzel (hairy) and Cassie–Baxter (super hairy). For the Wenzel‐type, a simple constant accounted for the increased droplet shatter caused by the hairs. For the Cassie–Baxter‐type, a cushioning factor was introduced to account for the absorption of kinetic energy at impact by the hair mat. The cushioning factor was estimated by measuring the relative height of the hair mat. By including these two parameters, the new model successfully predicted the mean adhesion of non‐hairy, hairy and super‐hairy plants (R² = 0.96). This model and the underlying principles determining hairy leaf adhesion developed in this article will help develop spray formulations effective at targeting hairy‐leaved weed and crop species.     

Improving cost‐effectiveness of brown rot control: the value of bio‐economic modelling*

Since 1995, the Dutch potato production chain has been hit by several outbreaks of brown rot, a quarantine disease caused by Ralstonia solanacearum race 3, biovar 2. To avoid establishment of brown rot in the potato production chain and avert the consequences on potato export, the Dutch government has implemented an intensive and costly control policy. It is unknown whether this policy is cost‐effective. A bio‐economic model was developed that can be used to simulate the effect of a control policy on the epidemiology and economic consequences of brown rot in the Dutch potato production chain. Two applications of this model are presented, based on which the potential contribution of the model to cost‐effective control of brown rot is discussed.     

Analysis of leaf wetness in the rice crop caused by dew formation—a simulation study1

Daily dew amount and dew duration were simulated using a multi-layer energy balance model. Two simplifications were made in the model and validated. First, the simulation results showed that daily weather data instead of hourly weather data could be used to simulate dew formation in crops, which was validated by measurements for short grass vegetation. Second, estimation of the total daily dew period was shown to be possible on the basis of nightly dew formation only. This simplification was possible on the basis of the existence of a linear relationship between the dew period after sunrise on the one hand and the dew amount at sunrise on the other. The multi-layer crop microweather model becomes much smaller and simpler (and is then called the SIMPLE model) if only night dew formation is simulated. Our results will make it possible to incorporate the SIMPLE model into a fungal disease epidemic model and a basic crop growth model.     

A refined lack‐of‐fit statistic to calibrate pesticide fate models for responsive systems

BACKGROUND: Calibration by inverse modelling was performed with the MACRO transport and fate model using long‐term (>10 years) drainflow and isoproturon (IPU) data from western France. Two lack‐of‐fit (LOF) indices were used to control the inverse modelling: sum of squares (SS) and an alternative statistic called the vertical‐horizontal distance integrator (VHDI), which is designed to account for offsets in observed and predicted arrival times of peak IPU concentration. With these data, SS was artificially inflated because it is limited to comparison of predicted and observed IPU concentrations that are concurrent in time. The LOFs were used along with the index of agreement (d) and the correlation coefficient (r) to ascertain the fit of the calibrated models. RESULTS: Predicted arrival times of peak IPU concentration differed somewhat from observed times. All four indices indicated better model fit for the second of two validation periods when inverse modelling was controlled by VHDI rather than SS (SS = 26.4, d = 0.660, r = 0.606 and VHDI = 1.25). The VHDI statistic was markedly lower compared with the uncalibrated model (38.0) and SS calibration results (24.5). The final maximum predicted IPU concentration (44.5 µg L^?1) for the calibration period was very similar to the observed value (44 µg L^?1). CONCLUSION: VHDI is seen as an effective alternative to SS for calibration and validation of pesticide fate models applied to responsive systems. VHDI provided a more realistic assessment of model performance for the transient flows and short‐lived concentrations observed here, and also effectively substituted for the objective function in inverse modelling. Copyright © 2009 Society of Chemical Industry     

The <Emphasis Type="Italic">Foc</Emphasis> gene governs resistance to race 3 of <Emphasis Type="Italic">Fusarium oxysporum</Emphasis> f. sp. <Emphasis Type="Italic">cucumerinum</Emphasis> in the cucumber cv. SMR-18

Bois noir (BN) is the most widespread European grapevine yellows disease caused by ‘Candidatus Phytoplasma solani’. Although our knowledge of the mechanisms of interactions of this pathogenic bacteria with host is largely unknown, the plant-pathogen system of BN is commonly used as a model system for studying grapevine yellows diseases. We applied here a conceptual model of general plant pathology – a disease triangle for describing interactions among the host plant, the pathogen and the environment. We generated a proof-of-concept statistical model for disease triangle using original experimental data and different statistical and data mining approaches for a selected system of ‘Ca. P. solani’ infection of cv. ‘Chardonnay’ grapevine plants. We monitored individual plants from a single vineyard over a period of six years. Phytoplasma content, the expression of 21 selected grapevine genes and environmental conditions were recorded and related to disease severity. Our model predicts that in described conditions BN is a function of the expression of grapevine gene VvDMR6, summer rainfall and abundance of ‘Ca. P. solani’. The greatest impact among elements of the disease triangle is attributed to the pathogen, and is independent of the pathogen titer. We showed that this first de facto representation of the disease triangle is useful for showing disease dynamics over several years and could be applied to other plant-pathogen systems. The overall results of this study will contribute to understanding of ‘Ca. P. solani’ biology and its interactions with grapevine host.     

Modelling within‐field spatial variability of crop biomass – weed density relationships using geographically weighted regression

The objective of this study is to offer a new framework for exploring and modelling the spatial variation in crop biomass – weed density relationships, adapting geographically weighted regression (GWR) to include a non‐linear regression model. The relationship between crop biomass and weed density is usually modelled by non‐linear regression models, in which the spatial heterogeneity of the relationship is ignored, although the effect of weeds on crop can differ in relation to topographic and edaphic variability. GWR attempts to capture spatial variability by calibrating a regression model to each location in space. We show the application of the method in different cereal cropping systems, with one or two weed species. The results indicate that GWR can significantly improve model fitting over non‐linear least squares (NLS) in some situations. Furthermore, the parameter estimates can be mapped to illustrate local spatial variations in the regression relationship under study and eventually to relate the spatial variability of the model to the environmental heterogeneity. We discuss the value of the GWR for analysing the observed spatial variability and for improving model development and our understanding of spatial processes.     

Segregation of non‐target‐site resistance to herbicides in multiple‐resistant Alopecurus myosuroides plants

Non‐target‐site resistance (NTSR) comprises a set of mechanisms conferring resistance to multiple modes of action. Investigation of the number of loci involved in NTSR will aid in the understanding of these resistance mechanisms. Therefore, six different multiple herbicide‐resistant Alopecurus myosuroides plants with different herbicide history were crossed in two generations with a susceptible wild type. Seeds from the backcrossing generation were studied for their segregation rate for resistance to five herbicides with four different modes of action (HRAC groups C₂, A, B and K₃). Taking into account that NTSR is a set of quantitative traits, the numbers of loci controlling NTSR were estimated using a normal mixture model fitted by the NLMIXED procedure of SAS. Each herbicide was controlled by a different number of loci comparing the six plants. In most of the cases, chlorotoluron resistance was controlled by one locus, whereas resistance to fenoxaprop‐P‐ethyl needed one or two loci. Resistance to pinoxaden was in all plants conferred by two loci. Cross‐resistance of fenoxaprop‐P‐ethyl and pinoxaden was found in all backcrossings, indicating that at least one of the two loci is responsible for both resistances. Resistance to mesosulfuron + iodosulfuron was conferred by a minimum of two loci. Results indicated that a minimum of five different loci can be involved in a multiple NTSR plant. Furthermore, the plant‐specific accumulation of NTSR loci was demonstrated. Such behaviour should be taken into account when evaluating the development and further spread of herbicide resistance.