Summary• Atmospheric CO 2 concentration is expected to increase by 50% near the middle of this century. The effects the free air CO 2 enrichment (FACE) is presented here on growth and development of field-grown grain sorghum ( Sorghum bicolor ) at ample (wet) and limiting (dry) levels of irrigation water at Maricopa, AZ, USA.• Daytime CO 2 mole fractions were 561 and 368 µ mol mol -1 for the FACE and control treatments, respectively. Irrigation plus precipitation averaged 1132 mm for the wet plots and 396 mm in the dry plots.• During the growing season, FACE increased biomass accumulation in the dry plots but the effects in the wet plots were inconsistent. At final harvest, FACE increased total yield from 999 to 1151 g m -2 in the dry plots and had no effect in the wet plots.• If atmospheric CO 2 continues to increase, total sorghum yield is likely to be higher in the future in areas where water is limited.

Crop models can be evaluated based on accuracy in simulating several years' yields for one location or on accuracy in simulating long‐term mean yields for several locations. Our objective was to see how the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model and a new version of CERES‐Maize (Crop‐Environment Resource Synthesis) simulate grain yield of rainfed maize (Zea mays L.). We tested the models at one county in each of nine states: Minnesota, New York, Iowa, Illinois, Nebraska, Missouri, Kansas, Louisiana, and Texas (MN, NY, IA, IL, NE, MO, KS, LA, and TX). Simulated grain yields were compared with grain yields reported by the National Agricultural Statistical Service (NASS) for 1983 to 1992. In each county we chose a soil commonly used in maize production, and we used measured weather data. Mean simulated grain yield for each county was always within 5% of the mean measured grain yield for the location. Within locations, measured grain yield was regressed on simulated grain yields and tested to see if the slope was significantly different from 1.0 and if the y‐intercept was significantly different from 0.0, both at the 95% confidence level. Only at MN, NY, and NE for ALMANAC and at MN, NY, and TX for CERES was slope significantly different from 1.0 or intercept significantly different from 0.0. The CVs of simulated grain yields were similar to the those of measured yields at most sites. Also, both models were appropriate for predicting an individual year's yield for most counties. Values for plant parameters, such as heat units for development and the harvest index, and values for soil parameters describing soil water‐holding capacity offer users reasonable inputs for simulating maize grain yield over a wide range of locations.

stricting their application in retrospective or validation studies (Hutchinson, 1991). Crop growth models require solar irradiance as input data, yetThe need for solar irradiance data for crop models there are few places where such data are routinely measured. For has led researchers to develop a number of methods for locations where measured values are not available, solar irradiance simulating such data. For example, some crop modelers can be estimated using empirical models such as the Bristow-(e.g., Rosenthal et al., 1989) have incorporated stochas-Campbell (B-C) model. This study was conducted to assess the spatial and seasonal accuracy of the B-C model for midcontinental locations tic weather generators into their simulations. These in Kansas. A 30-year data set from Manhattan, KS, was used to weather generators simulate irradiance and other metecalibrate and evaluate unmodified and modified forms of the B-C orological and climatological inputs based on probabilismodel. The effect of seasonality was investigated by subdividing the tic criteria. This approach eliminates the need for meayearly data into two subsets, a high noontime solar elevation angle sured solar irradiance; however, it seems reasonable period, ranging from DOY 121 to 273, and a low noontime elevation that estimated, rather than randomly generated, solar angle period comprising the remainder of the year. The B-C model irradiance values would also result in improved yield eswas also evaluated without seasonal division of the year. The calitimates. brated models were then tested against measured solar irradiance A number of techniques are available for estimating values for 10 sites distributed across the state of Kansas. Results solar irradiance. These vary in sophistication from simindicate that, for the calibration site at Manhattan, irradiance was ple empirical formulations based on common weather more accurately estimated using a modified form of the B-C model. For the yearly data, root mean square error (RMSE) was 3.9 MJ m Ϫ2 or climate data to complex radiative transfer schemes d Ϫ1 (25% error), compared with 5.2 MJ m Ϫ2 d Ϫ1 (24% error) for the that explicitly model the absorption and scattering of high solar elevation angle period and 3.6 MJ m Ϫ2 d Ϫ1 (32% error) the solar beam as it passes through the atmosphere. for the low solar elevation angle period. The RMSE for the 10 test Hall, Kansas State University, Manhattan, KS 66506-0801; R.L. Vanwhere A, B, and C are empirical coefficients. Although derlip,

Redroot pigweed is a common weed in sorghum fields throughout the southcentral United States including Kansas. In 1994 and 1995, field studies were conducted at two sites near Manhattan, KS, to determine the influence of redroot pigweed densities and times of emergence on sorghum yield and yield components. Redroot pigweed was sown at densities of 0.5, 1, 2, 4, and 12 plants meter−1of row within a 25-cm band over the sorghum row at planting and at the three- to four-leaf stage of sorghum. A rectangular hyperbola was used to describe the relationship between crop yield loss and weed density. Because of the instability of both coefficientsI(percentage yield loss at low weed density) andA(percentage yield loss at high weed density), our results do not support the use of a model based exclusively on weed number to estimate sorghum yield loss across all locations within a region. A quadratic polynomial equation that accounts for the time of weed emergence relative to the crop growth stage is suggested as an alternative method to estimate sorghum yield loss. At the densities studied, the time of pigweed emergence relative to the sorghum leaf stage was critical for the outcome of sorghum-pigweed competition. Significant sorghum yield losses occurred only when pigweed emerged before the 5.5-leaf stage of sorghum. An examination of yield components suggested that the yield loss was a result of a reduction in number of seeds per head.

Understanding how growth and development of grain sorghum [Sorghum bicolor (L.) Moench] genotypes respond to water limitation would provide a basis to assess the value of such responses in crop production and crop improvement. We conducted a greenhouse experiment to quantify responses of six genotypes to five levels of water limitation between floral initiation and flowering achieved by daily watering to weight. At flowering, the thermal time, biomass, height, water transpired, and transpiration efficiency (biomass/water transpired) were recorded. Results for water‐limited treatments were expressed relative to the well‐watered treatment. Degree of water limitation was indexed by calculating the ratio of the amount of water transpired in water‐limited treatments to that in the well‐watered treatment. This transpiration ratio decreases from a value of one as degree of water limitation increases. Under well‐watered conditions, genotypes differed significantly in thermal time and biomass, but not in height. Differences in biomass were related to differences in both water transpired and transpiration efficiency. The least efficient genotype had a value of transpiration efficiency 25% lower than that of the most efficient. Under water‐limiting conditions, relative thermal time increased at transpiration ratios < 0.55; genotypes did not differ. Relative height decreased linearly with transpiration ratio, and genotypes did not differ. Relative biomass decreased with increasing water limitation, but genotypes differed. This was related to genotypic differences in relative transpiration efficiency for transpiration ratios < 1. Some genotypes increased transpiration efficiency 28% under water limitation. The value of expressing water limitation as transpiration ratio and possible mechanisms explaining these findings are discussed.

Research with grain sorghum often involves sampling several times during the growth cycle. Samplings often are designated by calendar date, days after planting or emergence, or plant height. Often these bear little or no actual relationship to the morphological or physiological age or status of the plant. Although certain stages of sorghum growth are fairly well established, the growth cycle of sorghum has not been fully described. Therefore, a standard set of growth stages needs to be defined. Based on detailed studies of grain sorghum hybrids of different maturities, the following ten stages of development have been defined and illustrated: emergence, three‐leaf, five‐leaf, growing‐point, differentiation, final leaf visible in whorl, boot, half‐bloom, soft dough, hard dough, and physiological maturity. These stages are suggested as standards to describe the timing of sampling or treating sorghum.

In sorghum [Sorghum bicolor (L.) Moench], genotype‐by‐environment interaction effects on ontogeny can be caused by differing responses to temperature and photoperiod. We conducted glasshouse and field experiments to develop predictive models of ontogeny for old and new sorghum genotypes. New genotypes are considered better adapted to more tropical environments. In the glasshouse studies, 10 genotypes were grown at two temperatures (20 and 25 °C) and six planting dates (photoperiod 10 to 15 h). At photoperiods greater than about 13 h, duration of emergence to floral initiation (GS1) was lengthened about 5 d for all genotypes at both temperatures. Genotypes differed in duration of GS1 by up to 10 d at both temperatures. Hybrids responded like their earlier parent, indicating earliness to show some form of dominance. Photoperiod had little or no effect on duration of floral initiation to anthesis (GS2), and hybrids differed by about 3 d. Field experiments with 12 hybrids were conducted at sites in Australia and USA covering latitudes from 16 to 39 °C. Durations of GS1 and GS2 ranged from 17 to 128 d and 24 to 85 d, respectively. Daily rate of development was modeled using functions of air temperature and photoperiod. Development rate of all hybrids exhibited a curvilinear response to temperature in both phases. Old and new hybrids differed in their temperature responses in GS1 but were similar in GS2. New hybrids had slower rates of development at all temperatures, but the difference was greater at higher temperatures (>25 °C). All hybrids had similar short‐day photoperiodic response in GS1, with a critical photoperiod 13.2 h. The models were tested on a separate data set covering a similar broad range of environments and performed well.

Selection of winter wheat (Triticum aestivum L.) genotypes requires testing programs with complementary locations that sample environments of interest with minimal duplication. The goal of the current study was to improve prediction of genotype performance in the highly variable environments of the central Great Plains in the United States by estimating the contributions of genotype, location, and year to wheat yield variability and identifying subgroups of test locations that minimize crossover genotype‐by‐environment interaction. Variance components were estimated from Kansas wheat performance data from 17 locations from 1982 to 2002. Annual data sets balanced for genotypes and environments were used to generate genotype, genotype‐by‐environment biplots that could objectively separate locations into groups with the same top‐yielding genotype. Location, year, and their interaction introduced the greatest proportion of the variability in wheat performance test yields. Frequency of common grouping during the 21‐year period was used to construct six groups of test locations representing unique target environments. Evaluation of the six groups using results from two subsequent years revealed that they generally agreed with location groups observed in the previous 21 years. Smaller regional genotype‐by‐environment variance component estimates compared with statewide estimates further confirmed the effectiveness of the pro posed six regions for reducing genotype‐by‐environment interaction.