Wally Wilhelm

Phenological responses of wheat and barley to water and temperature: improving simulation models

Understanding and predicting small-grain cereal development is becoming increasingly important in enhancing management practices. Recent efforts to improve phenology submodels in crop simulations have focused on incorporating developmental responses to water stress and interpreting and understanding thermal time. The objectives of the present study were to evaluate data from three experiments to (a) determine the qualitative and quantitative response of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) to water stress and (b) ascertain where in space to measure temperature, to provide information required to improve phenological submodels. The first experiment tested the phenological responses of 12 winter wheat cultivars to water stress for two seasons at two sites. The second experiment tested the timing of water stress on spring barley phenological responses for 2 years. In a third experiment, soil near the shoot apex of field-grown spring wheat was heated to 3 °C above ambient soil temperature for three planting dates in each of 2 years, to test whether it is better to use soil or air temperature in calculating thermal time. The general response of wheat and barley to water stress was to reach growth stages earlier (i.e. to hasten development). The most significant response was for the grain filling period. Water stress had little effect on jointing and flag leaf complete/booting growth stages. Thermal time to jointing was highly variable across locations. However, thermal time to subsequent growth stages was very consistent both within and across locations. The winter wheat cultivars tested followed this general response across site-years, but inconsistencies were found, suggesting a complicated genotype by environment (G x E) interaction that makes improving phenology submodels for all cultivars difficult. The G x E interaction was most prominent for anthesis (A) and maturity (M) growth stages. Results of heating the soil at the shoot apex depth were completely unexpected: heating the soil did not speed spring wheat phenological development. These results, and others cited, suggest caution in allocating effort and resources to measuring or estimating soil temperature rather than relying on readily available air temperature as a means of universally improving phenology submodels. These results help quantify the response of wheat to water stress and thermal time for improving crop simulation models and management.

Developmental sequences for simulating crop phenology for water-limiting conditions

The timing, duration, and pace of developmental events, or phenology, are among the many responses of plants to limited soil water. Understanding and predicting plant responses to availability of soil water are important in improving the efficacy of management practices. However, the first steps towards gaining this understanding, summarising the complete developmental sequence of the shoot apex and correlating the timing of these events, have rarely been reported. Also, the effect of water-limiting conditions on crop phenology and shoot apex development is variable. The objective of this paper is to present the developmental sequence of the wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and corn (Zea mays L.) shoot apices and correlate events in these sequences with growth stages for both well-watered and water-limiting conditions. We note that phenological responses to water availability occur at 3 different scales: among crops, among cultivars of a crop, and among growth stages within a cultivar or crop. Clearly, genotype × environment interaction affects the accuracy of predicting phenology. However, the fact that plants develop in an orderly, predictable pattern allows a general foundation for synthesising the complete sequence of developmental events of the shoot apex and correlate these with growth stages when water is not limiting. These patterns and relationships are the foundation to build upon in quantifying our understanding of crop phenology under water-limiting environments.

The circuitous path to the comparison of simulated values from crop models with field observations

The Journal of Agricultural Science, Cambridge has been a fixture in dissemination of crop simulation models and the concepts and data upon which they are built since the inception of computers and computer modelling in the mid-20th century. To quantify the performance of a crop simulation model, model outputs are compared with observed values using statistical measures of bias, i.e. the difference between simulated and observed values. While applying these statistical measures is unambiguous for the experienced user, the same cannot always be said of determining the observed or simulated values. For example, differences in accessing crop development can be due to the subjectivity of an observer or to a definition that is difficult to apply in the field. Methods of determining kernel number, kernel mass, and yield can vary among researchers, which can add errors to comparisons between experimental observations and simulated results. If kernel moisture is not carefully determined and reported it can add error to values of grain yield and kernels per unit area regardless of the protocol used to collect these data. Inaccurate determination of kernel moisture will also influence computation of grain protein or oil content. Problems can also be associated with input data to the simulation models. Under-reporting of precipitation values from tipping bucket rain gauges, commonly found on automated weather stations, can introduce errors in results from crop simulation models. Using weather data collected too far from an experimental site may compound problems with input data. The importance of accurate soil and weather input data increases as the environment becomes more limiting for plant growth and development. Problems can also arise from algorithms that calculate important parameters in a model, such as daylength, which is used to determine a photoperiod response. Errors in the calculation of photoperiod can be related to the definition of sunrise and sunset and the inclusion or exclusion of civil twilight or to the improper calculation of the solar declination. Even the simple calculation of the daily mean air temperature can have an impact on the results from a non-linear algorithm. During a period when crop simulation modelling is moving in the difficult direction of incorporating genomic-based inputs, the critical importance of careful and accurate collection and reporting of field data and the need to develop robust algorithms that accommodate readily available or easily acquired input data should not be forgotten. As scientists we have an obligation to provide the best available knowledge and understanding as possible. Avoiding potential pitfalls will assist us as we develop new knowledge and understanding and incorporate these concepts into new or modified crop simulation models.

Corn stalk nitrate concentration profile: Implications for the end-of-season stalk nitrate test

The end-of-season corn (Zea mays L.) stalk nitrate-N test was developed as a post-mortem to determine if excessive or insufficient N was available to the corn crop during the latter part of the season. The stalk section specified for the test was very specific, the 20 cm-long section between 15 and 35 cm above the soil. Under production conditions, it may not always be possible to collect this precise stalk section. The objective of this study was to determine how nitrate concentration varied within the stalk from the soil level to the ear node, and how this variation could affect interpretations of the stalk nitrate test. Field grown (140 kg N ha−1) corn stalks were collected and separated into phytomers (the node plus leaf, internode, and bud developing from it). Phytomers were further divided into six segments; the node and five equal length segments of the internode. All samples were analysed for NO3-N with a nitrate-ion specific electrode after extraction with 0.04 M (NH4)2SO4. Nitrate concentrations of individual samples varied from less than 100 to greater than 8000 mg NO3-N kg−1 dry weight, and increased down the stalk from ear to soil. Generally, the nitrate concentrations of segments within a phytomer were similar. These results indicated new critical values, approximately 35% greater than the original ones, may be needed to determine if limiting or excessive amounts of N were available to the crop, i.e. 950 vs. 700 and 2700 vs. 2000 mg NO3-N kg−1 for insufficient and excessive levels, respectively. However, the general interpretation of test would remain unchanged because stalk nitrate concentrations vary so widely under field conditions from less than 100 to greater than 5000 mg NO3-N kg−1.