Wallace Wilhelm

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.

Crop and Soil Productivity Response to Corn Residue Removal: A Literature Review

of greenhouse gases in the atmosphere (IPCC, 2001), and ability of our agricultural systems to sustain producSociety is facing three related issues: overreliance on imported fuel, tion at rates needed to feed a growing world population increasing levels of greenhouse gases in the atmosphere, and producing sufficient food for a growing world population. The U.S. De(Cassman, 1999). Many papers have been written on partment of Energy and private enterprise are developing technology these topics both individually and in the various combinecessary to use high-cellulose feedstock, such as crop residues, for nations (Doran, 2002; Follett, 2001; Janzen et al., 1998a, ethanol production. Corn (Zea mays L.) residue can provide about 1998b; Lal et al., 1999). However, few authors have ad1.7 times more C than barley (Hordeum vulgare L.), oat (Avena sativa dressed all three topics together. L.), sorghum [Sorghum bicolor (L.) Moench], soybean [Glycine max Recent developments in the energy industry and ac(L.) Merr.], sunflower (Helianthus annuus L.), and wheat (Triticum tivity by entrepreneurs have prompted new strategies aestivum L.) residues based on production levels. Removal of crop for addressing the first issue, overreliance on imported residue from the field must be balanced against impacting the environfuels (Hettenhaus et al., 2000). This strategy expands use ment (soil erosion), maintaining soil organic matter levels, and preof biomass for fuel production and is contingent on deserving or enhancing productivity. Our objective is to summarize published works for potential impacts of wide-scale, corn stover collection velopment of new organisms or enzymes to convert on corn production capacity in Corn Belt soils. We address the issue of cellulosic (a high concentration of cellulose) biomass crop yield (sustainability) and related soil processes directly. However, [opposed to grain (starchy) biomass] to ethanol for use scarcity of data requires us to deal with the issue of greenhouse gases as a motor vehicle fuel. The U.S. DOE, in concert with indirectly and by inference. All ramifications of new management pracprivate enterprise, is making great strides toward develtices and crop uses must be explored and evaluated fully before an oping enzymes and improving efficiency in fuel producindustry is established. Our conclusion is that within limits, corn stover tion from biomass (DiPardo, 2000; Hettenhaus et al., can be harvested for ethanol production to provide a renewable, do2000). mestic source of energy that reduces greenhouse gases. RecommendaSources of cellulosic biomass are numerous (woody biotion for removal rates will vary based on regional yield, climatic mass crops and lumber industry wastes, forage crops, inconditions, and cultural practices. Agronomists are challenged to develop a procedure (tool) for recommending maximum permissible dustrial and municipal wastes, animal manure, and crop removal rates that ensure sustained soil productivity. residues); however, currently few sources are perceived to be available in sufficient quantity and quality to support development of an economically sized processing facility of about 1800 Mg dry matter d 1 (Hettenhaus T of the most pressing issues facing our society, et al., 2000), except crop residues (DiPardo, 2000). Bain the midterm, are overreliance on imported fuels gasse [remaining after sap extraction from sugarcane [U.S. Department of Energy (DOE) Office of Energy Ef(Saccharum officinarum L.)] in Louisiana and rice (Orficiency and Renewable Energy, 2002], increasing levels yza sativa L.) straw in California are regional examples of crop residues collected in current culture and availW.W. Wilhelm, USDA-ARS, 120 Keim Hall, Univ. of Nebraska, Linable for production of ethanol (DiPardo, 2000). Creatcoln, NE 68583-0934; J.M.F. Johnson, USDA-ARS, 803 Iowa Ave., ing an acceptable use or disposal procedure for these Morris, MN 56267-1065; J.L. Hatfield, 108 Natl. Soil Tilth Lab., 2150 residues represents a huge problem in the regions where Pammel Drive, Ames, IA 50011-3120; W.B. Voorhees, USDA-ARS (retired), 803 Iowa Ave., Morris, MN 56267-1065; and D.R. Linden, they are produced although the total quantity is not USDA-ARS (retired), 1991 Upper Buford Circle, St. Paul, MN 55108sufficient to have a great impact on fuel needs for the 0000. This paper is a joint contribution of the USDA-ARS and the nation (DiPardo, 2000). On the other hand, the quantity Agricultural Research Division of the University of Nebraska. Pubof corn stover is large, but corn stover is generally not lished as Journal Ser. no. 13949. Received 12 Dec. 2002. *Corresponding author (wwilhelm1@unl.edu). Abbreviations: 13C, change in 13C atom percent; DOE, Department Published in Agron. J. 96:1–17 (2004).  American Society of Agronomy of Energy; HI, harvest index; SOC, soil organic carbon; SOM, soil organic matter. 677 S. Segoe Rd., Madison, WI 53711 USA

Sustainable Biofuels Redux

Science-based policy is essential for guiding an environmentally sustainable approach to cellulosic biofuels.

Transmittance and Reflectance Measurements of Corn Leaves from Plants with Different Nitrogen and Water Supply

Summary Nitrogen is essential for crop production, but also contributes to eutrophication of surface waterand degradation of drinking water quality. Modern corn production requires relatively large quantities of N, which are generally supplied by fertilizers. Over-application of N fertilizers and animal wastes frequently results in nitrate leaching. Synchronizing N availability with crop N need offers the potential to protect the environment without sacrificing production. Tools are needed to rapidly and easily monitor crop N status to make timely decisions regarding fertilizer application. Analytical and optical techniques were evaluated with greenhouse grown corn at silking to evaluate several methods to monitor crop N status. A portable chlorophyll meter was used to measure chlorophyll content of leaves by means of transmittance measurements. Leaf N concentration and chlorophyll meter readings were positively correlated, but were also affected by water stress and hybrid differences. Water stress decreased chlorophyll meter readings but increased leaf N content and diffusive resistance. Nitrogen stress decreased leaf N concentration, chlorophyll meter readings, and diffusive resistance. Both water and N stresses affected crop reflectance measurements. Reflectance values in the green and near IR portions of the spectrum were inversely related to crop N status. Water stress increased reflectance in red, green, and near IR wavelengths. Water stress by N status interactions were significant for chlorophyll meter readings as well as reflectance measurements. Both leaf reflectance and chlorophyll meter measurements provided a good indication of N status for adequately watered plants, but the relationships were poor for plants grown under prolonged water stress.

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.

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.

Dryland Maize Development and Yield Resulting From Tillage and Nitrogen Fertilization Practices

Conservation tillage ( > 30% residue cover) has proven to be very effective in reducing runoff and erosion and in increasing soil water storage. In dryland cropping situations, the latter fact should result in a greater yield potential for conservation than for conventional tillage. In practice, however, this theoretical advantage has not consistently realized. The objective of this study was to determine the influence of tillage and N-fertilization management on growth and yield of maize (Zea mays L.) under dryland conditions in the western Corn Belt (U.S.A.). The experiment was conducted from 1977 through 1983 on a Crete-Butler silty clay loam (Pachic Arguistolls-Abruptic Argiaquolls). Whole-plot treatments were moldboard plow, disk, chisel plow, no-till, disk plus manure and no-till plus manure. Sub-plot treatments were N fertilization (NH4NO3) at 0, 70 or 140 kg ha−1N. Grain yield and yield components were not affected by the tillage × N-fertilization interaction. The response both to tillage and to N fertilization was influenced by yearly climatic variation. Generally, grain yield was maximum at 90 kg ha−1 N and, in dry years, yields usually declined at N rates > 90 kg ha−1 N. In only one year (1978) did tillage influence yield; the chisel plow treatment produced less grain than the moldboard plow or disk. The no-till treatment did not differ from the mean of the other 3 tillage practices in any year. The interaction of yearly weather variation with phenology and the development of the crop appeared to be a greater determinant of yield than tillage.

Evaluating SHOOTGRO 4.0 as a potential winter wheat management tool in the Czech Republic

Improving current cultural practices often involves more precise timing of the management activity based on crop development. Using crop simulation models to predict crop development and phenology has several problems. First, most existing models do not simulate sufficient developmental and phenological detail required to optimize selected management practices. Second, crop models normally emphasize the cultivars and conditions for the region in which they were developed, and may not generate satisfactory results when applied in new regions. Lastly, when users apply these models to new regions they often lack the specific data and knowledge of the model to adequately determine the crop parameters. Our objective was to assess whether the simulation model SHOOTGRO 4.0, which had the necessary level of developmental and phenological detail required for use as a management decision aid, could be easily and adequately parameterized to simulate winter wheat phenology and grain yield in the Czech Republic. We found that only a few parameters from the generic winter wheat cultivar used for the Central Great Plains in the USA needed to be changed, and the information needed to determine these few parameters were readily obtainable. The result was that the dates of anthesis and physiological maturity and final grain yield were predicted well at sites within the three major crop production regions of the Czech Republic. Sensitivity analysis also showed that the most sensitive management practices and initial conditions in SHOOTGRO are relatively easy to determine (e.g. sowing date, N fertilizer rate and timing, daily temperature), while it is not overly sensitive to those variables more difficult to determine (e.g. initial soil water in the profile). Based on this study, farmers and scientists needing wheat development information to increase the efficacy of their management practices can use SHOOTGRO 4.0 as a tool.