Nitrogen fertilizer continues to be the major input influencing corn ( Zea mays L.) yield in the Midwest. Improved N recommendations should result in greater N use efficiency and producer profit while reducing surface and groundwater contamination. This study was conducted to develop a plant‐based technique to detect and correct N deficiencies during the season. Chlorophyll meter readings and grain yield were collected from corn in irrigated monoculture corn and soybean [ Glycine max (L.) Merr.]–corn cropping systems with four hybrids and five N fertilizer application rates in the Platte Valley near Shelton, NE. Normalized chlorophyll meter readings (sufficiency index, SI) were calculated from data collected at three vegetative stages, defined by thermal time accumulation after planting, during each of the 10 yr of study (1995–2004). Highly significant linear correlations between SI and relative yield (normalized yield) indicated both responded similarly to N fertilizer application. Relationships between N rate and SI (at each of the three vegetative stages and combined over stages) were described by quadratic models. The combined model [(SI = 0.8073 + 0.002(N rate) − 0.0000056(N rate) 2 , R 2 = 0.70)] can be used to compute N needed to achieve maximum yield. Our procedure gives producers the tools to determine if N is needed, and if so, the amount of N required for maximum yield. In addition if SI is computed for specific areas of the field, N applications can be tailored to those areas, thereby reducing the potential of introducing more N into the system than needed to achieve maximum yield, with spatial and temporal constraints.
Society is facing three related issues: overreliance on imported fuel, increasing levels of greenhouse gases in the atmosphere, and producing sufficient food for a growing world population. The U.S. Department of Energy and private enterprise are developing technology necessary to use high‐cellulose feedstock, such as crop residues, for ethanol production. Corn ( Zea mays L.) residue can provide about 1.7 times more C than barley ( Hordeum vulgare L.), oat ( Avena sativ a L.), sorghum [ Sorghum bicolor (L.) Moench], soybean [ Glycine max (L.) Merr.], sunflower ( Helianthus annuus L.), and wheat ( Triticum aestivum L.) residues based on production levels. Removal of crop residue from the field must be balanced against impacting the environment (soil erosion), maintaining soil organic matter levels, and preserving or enhancing productivity. Our objective is to summarize published works for potential impacts of wide‐scale, corn stover collection on corn production capacity in Corn Belt soils. We address the issue of crop yield (sustainability) and related soil processes directly. However, scarcity of data requires us to deal with the issue of greenhouse gases indirectly and by inference. All ramifications of new management practices and crop uses must be explored and evaluated fully before an industry is established. Our conclusion is that within limits, corn stover can be harvested for ethanol production to provide a renewable, domestic source of energy that reduces greenhouse gases. Recommendation for removal rates will vary based on regional yield, climatic conditions, and cultural practices. Agronomists are challenged to develop a procedure (tool) for recommending maximum permissible removal rates that ensure sustained soil productivity.
Predicting the rate of leaf appearance, or phyllochron, aids in understanding and modeling grass development and growth. Nine equations predicting the phyllochron of wheat ( Triticum aestivum L.) were evaluated using field data from a variety of locations, cultivars, and management practices. Each equation is referred to by the last name of the first author; if there is more than one equation by the first author, additional descriptors were included. The BAKER and KIRBY equations predict the phyllochron based on changes in daylength following seedling emergence; CAO‐TEMP and CAO‐DAY use a curvilinear relationship with temperature and daylength, respectively; CAO‐T&D uses the ratio of temperature to daylength; VOLK mathematically refines CAO‐T&D; MIGLIETTA uses an ontogenetic decline in the rate of leaf appearance; and MIGLIETTA‐DAY adds photoperiod effects to MIGLIETTA. No equation adequately predicted the phyllochron. The r 2 values between predicted and measured phyllochron for winter wheat and spring wheat cultivars, respectively, were BAKER (0.001, 0.486), KIRBY (0.002, 0.487), CAO‐DAY (0.000, 0.174), MIGLIETTA‐DAY (0.013, 0.008), MIGLIETTA (0.002, 0.405), CAO‐TEMP (0.100, 0.190), CAO‐FIELD (0.078, 0.036), T&D (0.066, 0.030), and VOLK (0.119, 0.043). All equations predicted the phyllochron for spring wheat cultivars better than winter wheat cultivars. BAKER and MIGLIETTA showed no bias towards either over or underestimating the phyllochron; KIRBY tended to overestimate the phyllochron; and the remaining equations were biased towards underestimating the phyllochron. Equations developed from field data had the greatest range of predicted phyllochrons. Based on multiple criteria, the BAKER equation best predicted the phyllochron for the experimental data set. Other factors must be added to the equations to improve predictions. Much opportunity exists to improve our ability to predict the phyllochron.
Advanced biofuels will be developed using cellulosic feedstock rather than grain or oilseed crops that can also be used for food and feed. To be sustainable, these new agronomic production systems must be economically viable without degrading the soil and other natural resources. This review examines six agronomic factors that collectively define many of the limits and opportunities for harvesting crop residue for biofuel feedstock in the midwestern United States. The limiting factors include soil organic carbon, wind and water erosion, plant nutrient balance, soil water and temperature dynamics, soil compaction, and off-site environmental impacts. These are discussed in relationship to economic drivers associated with harvesting corn (Zea mays L.) stover as a potential cellulosic feedstock. Initial evaluations using the Revised Universal Soil Loss Equation 2.0 (RUSLE2) show that a single factor analysis based on simply meeting tolerable soil loss (T) might indicate that stover could be harvested sustainably, but the same analysis, based on maintaining soil organic carbon (SOC), shows the practice to be non-sustainable. Modifying agricultural management to include either annual or perennial cover crops is shown to meet both soil erosion and soil carbon requirements. The importance of achieving high yields and planning in a holistic manner at the landscape scale are also shown to be crucial for balancing limitations and drivers associated with renewable bioenergy production.
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.
Abstract Alfalfa ( Medicago sativa L.) breeders are constantly striving to improve the productivity of alfalfa. Yields have been increased primarily through the selection of plant materials resistant to insects and diseases. The objective of this study was to evaluate physiological characteristics which might be used as selection criteria in alfalfa improvement. Alfalfa clones were grown under field conditions on a Mojave clay loam soil. Carbon dioxide flux was evaluated in a closed system using an infrared gas analyzer. The physiological variables measured in these studies did not account for the variation in yield among the clones. Apparent photosynthetic rates, dark respiration rates, and postillumination CO 2 burst rates expressed as mg CO 2 dm −2 hour −1 were not correlated with dry matter production. However, when these physiological factors were multiplied by leaf area and expressed as total CO 2 exchange per plant per hour, there was a significant relationship between yield and these calculated variables, in both studies. Regression analyses of more than 30 factors indicated that leaf area, leaf to stem‐petiole ratio, and leaf weight per plant accounted for more than 95% of the variation in yield among alfalfa clones. The data from these studies suggest that morphological factors were more reliable indicators of alfalfa productivity than physiological factors.
