Gregory S. McMaster

Growing degree-days: one equation, two interpretations

Heat units, expressed in growing degree-days (GDD), are frequently used to describe the timing of biological processes. The basic equation used is GDD = [(TMAX + TMIN)2]−TBASE, where TMAX and TMIN are daily maximum and minimum air temperature, respectively, and TBASE is the base temperature. Two methods of interpreting this equation for calculating GDD are: (1) if the daily mean temperature is less than the base, it is set equal to the base temperature, or (2) if TMAX or TMIN < TBASE they are reset equal to TBASE. The objective of this paper is to show the differences which can result from using these two methods to estimate GDD, and make researchers and practitioners aware of the need to report clearly which method was used in the calculations. Although percent difference between methods of calculation are dependent on TMAX and TMIN data used to compute GDD, our comparisons have produced differences up to 83% when using a 0°C base for wheat (Triticum aestivum L.). Greater differences were found for corn (Zea mays L.) when using a base temperature of 10°C. Differences between the methods occur if only TMIN is less than TBASE, and then Method 1 accumulates fewer GDD than Method 2. When incorporating an upper threshold, as commonly done with corn, there was a greater difference between the two methods. Not recognizing the discrepancy between methods can result in confusion and add error in quantifying relationships between heat unit accumulation and timing of events in crop development and growth, particularly in crop simulation models. This paper demonstrates the need for authors to clearly communicate the method of calculating GDD so others can correctly interpret and apply reported results.

Estimation and evaluation of winter wheat phenology in the central Great Plains

Crop modeling and management requires accurate prediction of crop phenology. Phenology data for winter wheat (Triticum aestivum L.) were collected from seven sites in the central Great Plains for several years to relate phenological stages to environmental and cultural factors, and to provide needed phenology data for the central Great Plains. Number of calendar days (ND), growing degree-days (GDD), and photothermal units (PTU) were calculated for emergence (E), tiller initiation (TI), dormancy end (DE), jointing (J), heading (H), kernel in milk (KM), kernel in hard dough (KD), and maturity (M) using the Feekes growth scale for the main stem. Nine base temperatures (−2, 0, 1, 2, 3, 4, 5, 7, and 9°C) were used when accumulating GDD and PTU. Mean daily temperatures of 20, 25, and 30°C were used for upper thresholds. Accumulation of GDD, PTU and ND were calculated from planting date (S), E, and 1 January to the growth stage and from one growth stage to the next. Model sensitivity to soil water, cultivar, seeding rates, row spacing, rotation, and fertilizer were examined. The lower the base temperature for a model, the lower the root mean square error (RMSE) when beginning accumulation from S, E or 1 January, with −2°C the best except for DE, KD, and M where higher base temperatures tended to have lower RMSE. As M was approached, the 25°C upper threshold tended to do better than 20°C. Little difference was found between 25 and 30°C upper thresholds. The best model for predicting a stage varied, with ND the best for E through J. From H through M, PTU models had the lowest RMSE. Normally, GDD and PTU models beginning accumulation from 1 January outperformed models beginning accumulation from S or E. The GDD or PTU related to availability of soil water showed a parabolic relationship (concave downward) beginning at J and becoming more platykurtic as M was approached. Significant sensitivity to cultivar and row spacing/rotation was found, with occasional sensitivity by various model types found to fertilizer and planting date.

Simulating winter wheat shoot apex phenology

Simulation models are heuristic tools for integrating diverse processes and help to increase our understanding of complex processes and systems. Models that predict crop development can serve as decision-support tools in crop management. This paper describes a phenology simulation model for the winter wheat shoot apex and reports validation and sensitivity analysis results. The complete developmental sequence of the winter wheat shoot apex is quantitatively outlined and correlated with commonly recognised phenological growth stages. The phyllochron is used to measure the thermal time between most phenological growth stages, thereby increasing the flexibility over the growing degree-day (GDD) and photothermal approaches. Nineteen site-years covering a range of climatic conditions, cultural practices and cultivars across the Central Great Plains, USA, are used to validate the model. Validation results show that the predicted phyllochron (108 GDD) agrees well with the observed phyllochron (107 GDD) for ten cultivars. Mean seedling emergence is predicted to within 2 days in almost all of the 19 site-years. The ability of the model to predict growth stages accurately increased successively from jointing to heading to maturity. Maturity is generally predicted to within 5 days of the observed day. After validation, recalibration of the phyllochron estimates between growth stages are provided, and corrections for mesic and xeric conditions are suggested. Further validation of the entire developmental sequence of the shoot apex is recommended.

Elevated CO2 further lengthens growing season under warming conditions.

Observations of a longer growing season through earlier plant growth in temperate to polar regions have been thought to be a response to climate warming. However, data from experimental warming studies indicate that many species that initiate leaf growth and flowering earlier also reach seed maturation and senesce earlier, shortening their active and reproductive periods. A conceptual model to explain this apparent contradiction, and an analysis of the effect of elevated CO2—which can delay annual life cycle events—on changing season length, have not been tested. Here we show that experimental warming in a temperate grassland led to a longer growing season through earlier leaf emergence by the first species to leaf, often a grass, and constant or delayed senescence by other species that were the last to senesce, supporting the conceptual model. Elevated CO2 further extended growing, but not reproductive, season length in the warmed grassland by conserving water, which enabled most species to remain active longer. Our results suggest that a longer growing season, especially in years or biomes where water is a limiting factor, is not due to warming alone, but also to higher atmospheric CO2 concentrations that extend the active period of plant annual life cycles.

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.

Simulating the influence of vernalization, photoperiod and optimum temperature on wheat developmental rates

BACKGROUND AND AIMS Accurately representing development is essential for applying crop simulations to investigate the effects of climate, genotypes or crop management. Development in wheat (Triticum aestivum, T. durum) is primarily driven by temperature, but affected by vernalization and photoperiod, and is often simulated by reducing thermal-time accumulation using vernalization or photoperiod factors or limiting accumulation when a lower optimum temperature (T(optl)) is exceeded. In this study T(optl) and methods for representing effects of vernalization and photoperiod on anthesis were examined using a range of planting dates and genotypes. METHODS An examination was made of T(optl) values of 15, 20, 25 and 50 degrees C, and either the most limiting or the multiplicative value of the vernalization and photoperiod development rate factors for simulating anthesis. Field data were from replicated trials at Ludhiana, Punjab, India with July through to December planting dates and seven cultivars varying in vernalization response. KEY RESULTS Simulations of anthesis were similar for T(optl) values of 20, 25 and 50 degrees C, but a T(optl) of 15 degrees C resulted in a consistent bias towards predicting anthesis late for early planting dates. Results for T(optl) above 15 degrees C may have occurred because mean temperatures rarely exceeded 20 degrees C before anthesis for many planting dates. For cultivars having a strong vernalization response, anthesis was more accurately simulated when vernalization and photoperiod factors were multiplied rather than using the most limiting of the two factors. CONCLUSIONS Setting T(optl) to a high value (30 degrees C) and multiplying the vernalization and photoperiod factors resulted in accurately simulating anthesis for a wide range of planting dates and genotypes. However, for environments where average temperatures exceed 20 degrees C for much of the pre-anthesis period, a lower T(optl) (23 degrees C) might be appropriate. These results highlight the value of testing a model over a wide range of environments.

Computer use in agriculture: an analysis of Great Plains producers☆

Computers have changed a great deal in the past decade, yet the last survey of computer use in agriculture was performed in 1991. Furthermore, previous computer use surveys are not very extensive in coverage. In the summer and fall of 1996, we conducted a random survey of Great Plains producers. The purpose of the survey was to examine three questions: (1) who adopts computers and what are they and their farms like; (2) what are the characteristics of non-adopters; and (3) what tasks do producers want computers to perform? Our results confirmed that most of the variables earlier studies identified as influential on computer adoption still had an impact. These included farm size (acres and sales), ownership of livestock, farm tenure, and off-farm employment exposure to computer use. We found some question as to whether age or experience is a better predictor of computer adoption. Moreover, there also appears to be reason to question whether education has a significant impact on adoption.

Soil management alters seedling emergence and subsequent autumn growth and yield in dryland winter wheat–fallow systems in the Central Great Plains on a clay loam soil

Stand establishment and subsequent autumn development and growth are important determinants of winter wheat (Triticum aestivum L.) yield. Soil management practices change soil properties and conditions, which alter seedling emergence, crop development and growth. Pre-plant soil management practices were studied for 6 years in a wheat–fallow rotation in eastern Colorado, USA, to isolate the impacts of pre-plant tillage (PT) and residue level on winter wheat seedling emergence and autumn development and growth. A split plot design was used with PT, using a moldboard plow that incorporated surface residue, and with no-tillage (NT). The tillage systems represented the main plots and three residue levels within each tillage treatment as subplots: no residue (0R), normal residue (1R) and twice-normal residue (2R). Residue amount had little effect on emergence or autumn growth and development. PT resulted in soil water loss from the plow zone. NT plots had more favorable soil water levels in the seeding zone which resulted in faster, more uniform and greater seedling emergence in 4 out of the 6 years. This is especially critical for stand establishment in years with low rainfall after planting. Soil or air temperature did not account for differences among treatments. Earlier and greater seedling emergence in NT treatments resulted in greater autumn development and growth. Shoot biomass, tiller density and leaf numbers were greater in NT, and again residue amount had little effect. At spring green-up, NT treatments had greater soil water in the profile. Grain yield was always equal or greater in NT than in PT, and positively correlated with earlier/greater seedling emergence and autumn growth. NT will enhance soil protection and likely increase snow catch, reduce evaporation and benefit yield in semiarid eastern Colorado.

Above-ground vegetative development and growth of winter wheat as influenced by nitrogen and water availability

Abstract Assessing the influence of nitrogen and water availability on development and growth of individual organs of winter wheat (Triticum aestivum L.) is critical in evaluating the response of wheat to environmental conditions. We constructed a simulation model (SHOOTGRO 2.0) of shoot vegetative development and growth from planting to early boot by adding nitrogen and water balances and response functions for seedling emergence, tiller and leaf appearance, leaf and internode growth, and leaf and tiller senescence to the existing wheat development and growth model, SHOOTGRO 1.0. Model inputs include daily maximum and minimum air temperature, rainfall, daily photosynthetically active radiation, soil characteristics necessary to compute soil N and water balances, and several factors describing the cultivar and soil conditions at planting. The model provides information on development and growth characteristics of up to six cohorts of plants within the canopy (cohort groupings are based on time of emergence). The cohort structure allows SHOOTGRO 2.0 to provide output on the frequency of occurrence of plants with specific features (tillers and leaves) within the canopy. The model was constructed so that only water availability limited seedling emergence. Resource availability (nitrogen and water) does not influence time of leaf appearance. Leaf and internode growth, and leaf and tiller senescence processes are limited by the interaction of N and water availability. Tiller appearance is influenced by the interaction of N, radiation and water availability. Predicted and observed dates of emergence and appearance of the first tiller had correlation coefficients of 0.98 and 0.93, respectively. However, these events were, on average, predicted 3.2 and 5.2 days later than observed. SHOOTGRO 2.0 generally under-predicted the number of culms per unit land area, partially because the simulation is limited to a maximum of 16 culms/plant. Model output shows that the simulation is sensitive to N and water inputs. The model provides a tool for predicting vegetative development and growth of the winter wheat with individual culms identified and followed from emergence through boot. SHOOTGRO 2.0 can be used in evaluating alternative crop management strategies.

Simulating winter wheat spike development and growth

McMaster, G.S., Morgan, J.A. and Wilhelm, W.W., 1992. Simulating winter wheat spike development and growth. Agric. For. Meteorol., 60: 193-220. Mechanistic crop simulation models can aid in integrating and directing research, and in improving farm management strategies. Information derived from recent research on spike development and growth of winter wheat (Triticum aestivum L.) was incorporated into a submodel, SPIKEGRO, and added to an existing model called SHOOTGRO. This manuscript discusses the SPIKEGRO submodel. SPIKEGRO emphasizes the reproductive functioning of the shoot apex. The complete developmental sequence of the shoot apex is outlined and quantified. All developmental events and growth stages are predicted, most using the phyllochron approach. Spikelet and floret primordium initiation, growth, and abortion; ovule fertilization and growth; and rachis and chaff growth are simulated on morphologicallyidentified culms. The phyllochron interval, rather than growing degree-days, is used throughout the model to increase flexibility in predicting yearly and within-stand variation in development. Up to six cohorts of plants are simulated simultaneously from time of emergence, using a daily time step. Initial inputs consist of general agronomic information such as planting date, density, and depth, site latitude, cultivar heightclass, soil water and N concentration, and soil characteristics (e.g. bulk density, organic carbon, parameters for a water-release curve). Cultivar differences, if known, can be incorporated by changing the input parameter file. Validation results and sensitivity analysis suggested six modifications that should improve model realism and predictions. Most of the modifications are easy corrections of simplified algorithms. SPIKEGRO integrates aboveground development and growth of individual plant components into one simulation. The model is useful in estimating development and growth throughout the growing season, and in predicting all stages of shoot apex development critical in scheduling cultural practices.