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The article discusses a study aimed at developing a set of euthanasia guidelines based on specific behavioural parameters to complement current clinical and physiological changes when making critical end-of-life decisions. It focuses on the captive non-human primates although the methodology can be modified for other animals with unique and readily observable behavioural characteristics. It notes that monitoring animal behaviour is an invaluable assessment tool for managing captive animals.

Presents the short story "Rain and Sparrow," by G. Leigh Wilkerson.

Crop model testing in diverse environments is essential if modelers wish to make applications or extrapolations to those environments. A recent study demonstrated the effectiveness of optimization techniques for deriving cultivar coefficients for the CROPGRO‐Soybean model from typical information provided by soybean performance tests. The objectives of this study were (i) to explore the extent to which cultivar coefficients developed by these approaches from crop performance tests are stable across different regions, (ii) to test the CROPGRO‐Soybean model's ability to predict phenology and seed yield using cultivar coefficients that were developed in different regions, and (iii) to investigate whether 3 yr of crop performance data are adequate for developing stable genetic coefficients. A stepwise procedure was applied to derive cultivar coefficients for 10 common cultivars grown in different environments in Georgia and North Carolina. Regarding the transportability of cultivar coefficients across states, we found that the critical daylength coefficients were the most reliable cultivar traits. We found less stability of the cultivar traits that control genetic differences in seed yield potential. The estimated cultivar coefficients developed in Georgia enabled CROPGRO to predict yield and harvest maturity in North Carolina within 3.8% and 3.5 d, respectively, from the observed averages. Using the cultivar coefficients developed from North Carolina environments allowed us to simulate the actual mean yield and harvest maturity in Georgia to within 2.5% and 2.0 d. Furthermore, the model's ability to predict seed yield and maturity with cultivar coefficients developed from 3 yr of data was nearly as good as that derived from much larger data sets.

To efficiently use postemergence herbicides, decision makers must determine when weed populations exceed economic treatment thresholds. An interactive microcomputer program named HERB has been developed to help evaluate potential crop damage from multi‐species weed complexes in soybean [Glycine max(L.) Merr.] and determine the appropriate course of action. Seventy‐six weed species were rated on a scale of zero to 10 according to their competitiveness with soybean. Potential yield loss is estimated from these rankings, the number of weeds of each species present in the field, and expected weed‐free yield. The recommendation whether to apply a herbicide and, if so, which one, is based on herbicide cost and efficacies under different conditions and expected soybean selling price. Alternative herbicide choices are ranked according to expected net return. HERB is intended to provide the latest research information in an organized and easily usable format. The approach should be applicable to other crops and pests.

The yield reduction caused by a particular weed species depends not only on weed density, but also on many other factors which affect both crop and weed growth through time. The objective of this study was to develop a simulation model of crop/weed competition which could be used to investigate the effects of environmental conditions on weed and crop growth. A previously developed soybean crop growth model was used to simulate the growth of soybean [Glycine max(L.) Merr.]. A weed growth model was developed and coupled with the soybean model to describe growth of weed and soybean growing together and competing for resources. Published data from a field experiment were used to determine model parameters for common cocklebur (Xanthium strumariumL.). In the combined soybean‐weed model, only competition for light and water are considered. Competition for light occurs within an “area of influence” around each weed. The size of this area depends on weed canopy diameter, which increases as a function of weed leaf area. Competition for water has been modeled by using a field average total leaf area (weeds and soybean) in the calculation of potential transpiration. The model developed from 1 yr of data fit data from another year reasonably well. The approach developed in this research is a practical way for modeling crop/weed competition which incorporates both the effect of environment on growth processes and the nonuniform effect of a weed on crop plants at different distances from the weed.

In order to accurately model the flowering process, it is essential to determine which environmental factors are affecting development at any point in time. The purpose of this research was to determine when different soybean [Glycine max(L.) Merr.) genotypes first become sensitive to photoperiod and how long photoperiod continues to influence the appearance of the first flower under optimal temperature conditions. Controlled‐environment experiments were conducted in which plants were switched at intervals between emergence and flowering from long‐day (22 h) to short‐day (9 h) treatments and from short‐ to long‐day treatments. AH photoperiod chambers were maintained at 26 °C both day and night. Six cultivars from six maturity groups were used: ‘Dawson’, ‘Williams’, ‘Ransom’, ‘Forrest’, ‘Davis’, and ‘Jupiter’. Two nearly isogenic breeding lines differing in response to inductive conditions were also evaluated. All cultivars tested were sensitive to photoperiod by the time the unifoliolate leaf was fully expanded. The experimental line bred for a lengthened juvenile phase exhibited apparent insensitivity to photoperiod for 11 d longer than any of the other genotypes. After flowering was induced, further inductive nights hastened flowering. for all cultivars, photoperiod during the last 6.3 to 8.7 d prior to expression of the first flower had no effect on time to first flower. Thus, for modeling purposes, the interval between emergence and first flower can be divided into four phases: (1) a purely vegetative phase (absent in most of the cultivars tested); (2) a photoperiod‐sensitive inductive phase; (3) a photoperiod‐sensitive post‐inductive phase; and (4) a photoperiod‐insensitive post‐inductive phase.

Defoliation experiments were performed in order to provide information for development of a pest management simulation model for peanuts (Arachis hypogaeaL.). ‘Florunner’ peanut plants were defoliated uniformly and nonuniformly at 9, 12, and 16 weeks after planting and harvested 2, 4, or 6 weeks following treatment. All defoliations resulted in lower stem weight to length ratios, lower pod numbers and weights, and equal or higher leaf numbers and weights. Defoliation altered the normal partitioning of photosynthate between plant parts. The location of defoliation damage (either uniform throughout the canopy or confined to the outer portion of the canopy) affected plant response in that plants defoliated nonuniformly grew more leaves in the 2 weeks following treatment than those defoliated uniformly

A velvetbean caterpillar (VBC), Anticarsia gemmatalis Hübner, population model was developed for use in a pest management program. The processes of oviposition, development, mortality, and immigration are modeled. Temperature effects on oviposition and development rates are included, as well as differences in individual response to temperature. Information in the literature and field data were used to set the parameters of the model, with simulated larval populations compared with field larval populations for 1 year. Early season population dynamics were well described by the model for an additional 2 years. Late-season population dynamics were well described in only 1 of the other 2 years, due to an outbreak 1 year of the entomogenous fungus Nomuraea rileyi Farlow (Sampson), which caused the VBC population to crash. A Nomuraea model is needed to improve predictions when weather conditions are favorable for the disease. Sensitivity analyses indicated that accurate estimates of adult influx into the field are important to predict population dynamics of VBC within the field.