Water use efficiency (WUE) is defined as the amount of carbon assimilated as biomass or grain produced per unit of water used by the crop. One of the primary questions being asked is how plants will ...respond to a changing climate with changes in temperature, precipitation, and carbon dioxide (CO
) that affect their WUE At the leaf level, increasing CO
increases WUE until the leaf is exposed to temperatures exceeded the optimum for growth (i.e., heat stress) and then WUE begins to decline. Leaves subjected to water deficits (i.e., drought stress) show varying responses in WUE. The response of WUE at the leaf level is directly related to the physiological processes controlling the gradients of CO
and H
O, e.g., leaf:air vapor pressure deficits, between the leaf and air surrounding the leaf. There a variety of methods available to screen genetic material for enhanced WUE under scenarios of climate change. When we extend from the leaf to the canopy, then the dynamics of crop water use and biomass accumulation have to consider soil water evaporation rate, transpiration from the leaves, and the growth pattern of the crop. Enhancing WUE at the canopy level can be achieved by adopting practices that reduce the soil water evaporation component and divert more water into transpiration which can be through crop residue management, mulching, row spacing, and irrigation. Climate change will affect plant growth, but we have opportunities to enhance WUE through crop selection and cultural practices to offset the impact of a changing climate.
Temperature is a primary factor affecting the rate of plant development. Warmer temperatures expected with climate change and the potential for more extreme temperature events will impact plant ...productivity. Pollination is one of the most sensitive phenological stages to temperature extremes across all species and during this developmental stage temperature extremes would greatly affect production. Few adaptation strategies are available to cope with temperature extremes at this developmental stage other than to select for plants which shed pollen during the cooler periods of the day or are indeterminate so flowering occurs over a longer period of the growing season. In controlled environment studies, warm temperatures increased the rate of phenological development; however, there was no effect on leaf area or vegetative biomass compared to normal temperatures. The major impact of warmer temperatures was during the reproductive stage of development and in all cases grain yield in maize was significantly reduced by as much as 80−90% from a normal temperature regime. Temperature effects are increased by water deficits and excess soil water demonstrating that understanding the interaction of temperature and water will be needed to develop more effective adaptation strategies to offset the impacts of greater temperature extreme events associated with a changing climate.
Wheat production is required to supply food for the world's population, and increases in production will be necessary to feed the expanding population. Estimates show that production must increase by ...1 billion metric tons to meet this demand. One method to meet future demand is to increase wheat yields by reducing the gap between actual and potential yields. Potential yields represent an optimum set of conditions, and a more realistic metric would be to compare actual yields with attainable yields, where these yields represent years in the record where there is no obvious limitation. This study was conducted to evaluate the yield trends, attainable yields, and yield gaps for the 10 largest wheat producing countries in the world and more localized yield statistics at the state or county level. These data were assembled from available government sources. Attainable yield was determined using an upper quantile analysis to define the upper frontier of yields over the period of record and yield gaps calculated as the difference between attainable yield and actual yield for each year and expressed as a percentage of the attainable yield. In all countries, attainable yield increase over time was larger than the yield trend indicating the technological advances in genetics and agronomic practices were increasing attainable yield. Yield gaps have not shown a decrease over time and reflect that weather during the growing season remains the primary limitation to production. Yield gap closure will require that local producers adopt practices that increase their climate resilience in wheat production systems.
Cereal production around the world is critical to the food supply for the human population. Crop productivity is primarily determined by a combination of temperature and precipitation because ...temperatures have to be in the range for plant growth and precipitation has to supply crop water requirements for a given environment. The question is often asked about the changes in productivity and what we can expect in the future and we evaluated the causes for variation in historical annual statewide wheat grain yields in Oklahoma, Kansas, and North Dakota across the Great Plains of United States. Wheat (
L.) is adapted to this area and we focused on production in these states from 1950 to 2016. This analysis used a framework for annual yields using yield gaps between attainable and actual yields and found the primary cause of the variation among years were attributable to inadequate precipitation during the grain-filling period. In Oklahoma, wheat yields were reduced when April and May precipitation was limited (
= 0.70), while in Kansas, May precipitation was the dominant factor (
= 0.78), and in North Dakota June-July precipitation was the factor explaining yield variation (
= 0.65). Temperature varied among seasons and at the statewide level did not explain a significant portion of the yield variation. The pattern of increased variation in precipitation will cause further variation in wheat production across the Great Plains. Reducing yield variation among years will require adaptation practices that increase water availability to the crop coupled with the positive impact derived from other management practices, e.g., cultivars, fertilizer management, etc.
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ...ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24–38% for the different end‐of‐season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in‐season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e‐mean) or median (e‐median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e‐median ranked first in simulating measured GY and third in GPC. The error of e‐mean and e‐median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops ...(corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management practices that varied rate and timing. Six different vegetation indices were found most useful, depending on crop phenology and management practices: (a) simple ratio for biomass, (b) NDVI for intercepted PAR, (c) SAVI for early stages of LAI, (d) EVI for later stages of LAI, (e) CIgreen for leaf chlorophyll, (f) NPCI for chlorophyll during later stages, and (g) PSRI to quantify plant senescence. There were differences among varieties of corn and soybean for the vegetation indices during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics.
Climate change affects all segments of the agricultural enterprise, and there is mounting evidence that the continuing warming trend with shifting seasonality and intensity in precipitation will ...increase the vulnerability of agricultural systems. Agricultural is a complex system within the USA encompassing a large number of crops and livestock systems, and development of indicators to provide a signal of the impact of climate change on these different systems would be beneficial to the development of strategies for effective adaptation practices. A series of indicators were assembled to determine their potential for assessing agricultural response to climate change in the near term and long term and those with immediate capability of being implemented and those requiring more development. The available literature reveals indicators on livestock related to heat stress, soil erosion related to changes in precipitation, soil carbon changes in response to increasing carbon dioxide and soil management practices, economic response to climate change in agricultural production, and crop progress and productivity. Crop progress and productivity changes are readily observed data with a historical record for some crops extending back to the mid-1800s. This length of historical record coupled with the county-level observations from each state where a crop is grown and emerging pest populations provides a detailed set of observations to assess the impact of a changing climate on agriculture. Continued refinement of tools to assess climate impacts on agriculture will provide guidance on strategies to adapt to climate change.
For maize (Zea mays L.), nitrogen (N) fertilizer use is often summarized from field to global scales using average N use efficiency (NUE). But expressing NUE as averages is misleading because grain ...increase to added N diminishes near optimal yield. Thus, environmental risks increase as economic benefits decrease. Here, we use empirical datasets obtained in North America of maize grain yield response to N fertilizer (n = 189) to create and interpret incremental NUE (iNUE), or the change in NUE with change in N fertilization. We show for those last units of N applied to reach economic optimal N rate (EONR) iNUE for N removed with the grain is only about 6%. Conversely stated, for those last units of N applied over 90% is either lost to the environment during the growing season, remains as inorganic soil N that too may be lost after the growing season, or has been captured within maize stover and roots or soil organic matter pools. Results also showed iNUE decrease averaged 0.63% for medium-textured soils and 0.37% for fine-textured soils, attributable to fine-textured soils being more predisposed to denitrification and/or lower mineralization. Further analysis demonstrated the critical nature growing season water amount and distribution has on iNUE. Conditions with too much rainfall and/or uneven rainfall produced low iNUE. Producers realize this from experience, and it is uncertain weather that largely drives insurance fertilizer additions. Nitrogen fertilization creating low iNUE is environmentally problematic. Our results show that with modest sub-EONR fertilization and minor forgone profit, average NUE improvements of ~10% can be realized. Further, examining iNUE creates unique perspective and ideas for how to improve N fertilizer management tools, educational programs, and public policies and regulations.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Capture of solar radiation by plant canopies and conversion of this energy into biomass or grain has been described as radiation use efficiency. Radiation use efficiency (RUE) has been developed as a ...function of biomass accumulation and intercepted photosynthetically active radiation (IPAR) by the canopy and evaluated under conditions not limiting to plant growth. This study was designed to evaluate RUE across a number of corn (Zea mays L.) and soybean Glycine max (L.) Merr. experiments in central Iowa with various tillage and management inputs to determine how RUE changed as a function of agronomic management. In these studies, IPAR was derived through the use of a normalized difference vegetation index, which is a surrogate for the photosynthetically active radiation canopy interception term, and total canopy biomass was measured at five times during the growing season from the early vegetative stage through maturity. Grain yields were obtained from each plot with both hand‐ harvested and combine samples. Values for RUE showed a linear relationship between biomass and IPAR for the entire growing season, with the better fit when the relationship was confined to the vegetative development period. There were differences among years, with management and tillage systems affecting RUE values through the differences in biomass or grain yield. Grain yield relationships to IPAR showed more variation than total biomass, with yearly differences evident in the amount of IPAR during the grain‐filling period suggesting that the length of the grain‐filling period is a dominant factor in grain yield. Application of RUE to field‐scale observations will provide a method of assessing where crop productivity is limited.
Remote sensing has proven to provide agriculture with many different assessments for crop vigor and productivity. The continual evolution of remote sensing instrumentation and platforms has provided ...new opportunities to use these tools in the assessment of agricultural systems. The application of remote sensing to quantify the spatial variation in production fields across the Midwest over multiple years has revealed there are three stability zones: the high yielding stable zone, the low yielding stable zone, and the unstable zone. These are derived using a combination of thermal images to detect areas of water stress and the normalized difference vegetative index to assess crop vigor and efficiency of light capture. Development of tools using remote sensing coupled with artificial intelligence and machine learning can transform agriculture through the ability to identify variable areas within fields but also determine the potential adaptive strategies to increase the profitability for each field while reducing the environmental impact through more efficient use of nutrients and pesticides. Development of new tools using remote sensing fulfills the vision of integrating many sources of information into decision making at the field and farm scale.
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