Detailed plant and soil thermal regime in agronomy.
Plant and soil thermal behavior for various conditions of crop species, plant spacing, tillage, irrigation and aerial thermal scanner imagery
Gerbermann, A. H.↗
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Jennifer C Wei↗
Meteorological models for estimating phenology of corn
Knowledge of when critical crop stages occur and how the environment affects them should provide useful information for crop management decisions and crop production models. Two sources of data were evaluated for predicting dates of silking and physiological maturity of corn (Zea mays L.). Initial evaluations were conducted using data of an adapted corn hybrid grown on a Typic Agriaquoll at the Purdue University Agronomy Farm. The second phase extended the analyses to large areas using data acquired by the Statistical Reporting Service of USDA for crop reporting districts (CRD) in Indiana and Iowa. Several thermal models were compared to calendar days for predicting dates of silking and physiological maturity. Mixed models which used a combination of thermal units to predict silking and days after silking to predict physiological maturity were also evaluated. At the Agronomy Farm the models were calibrated and tested on the same data. The thermal models were significantly less biased and more accurate than calendar days for predicting dates of silking. Differences among the thermal models were small. Significant improvements in both bias and accuracy were observed when the mixed models were used to predict dates of physiological maturity. The results indicate that statistical data for CRD can be used to evaluate models developed at agricultural experiment stations.
Daughtry, C. S. T.↗
Enhancing Global Food Security: Opportunities for the American Meteorological Society
Food security is a key pillar of environmental security yet remains one of the world’s greatest challenges. Its obverse, food insecurity, negatively impacts health and well-being, drives mass migration, and undermines national security and global sustainable development. Ensuring food security is a delicate balance of myriad concerns within the atmospheric and Earth sciences, agronomy and agriculture engineering, social sciences, economics, monitoring, and policymaking. A Food Security Presidential Session at the American Meteorological Society’s (AMS) 2022 Annual Meeting brought together experts across disciplines to tackle issues at the nexus of weather, climate, and food security. The starkest takeaway was the realization that, despite its importance and clear roles for the atmospheric and climate sciences, food security has not been a focus for the AMS community. The aim of this paper is to build on the perspectives shared by this expert panel and to identify overlapping issues and key points of intersection between the food-security community and AMS. We examine 1) the interactions between weather, climate, and the food system and how they influence food security; 2) the time and spatial scales of food security decision support that match weather and climate phenomena; 3) the role of both providers and users of information as well as decision-makers in improving research to operations for food security; and 4) the opportunities for the AMS community to address food security. We conclude that, moving forward, the AMS community is well-positioned to scale up its engagement across the global food system to address existing scientific needs and technology gaps to improve global food security.
Food security↗
Earth study from space
The significance that space studies are making to all Earth sciences in the areas of geography, geodesy, cartography, geology, meteorology, oceanology, agronomy, and ecology is discussed. It is predicted that cosmonautics will result in a revolution in science and technology.
Sidorenko, A. V.↗
Brazilian Satellite Remote Sensing program selected recent applications
Applications in the areas of agronomy, geology, oceanography, geography, environment and cartography are presented. A brief description of the Brazilian program is also given.
Dejesusparada, N.↗
Laboratory exercises, remote sensing of the environment
The exercises are designed to convey principles and theory of remote sensing, and methodologies of its application to civil engineering and environmental concerns, including agronomy, geography, geology, wildlife, forestry, hydrology, and other related fields. During the exercises the student is introduced to several types of remote sensing represented by imagery from conventional format: panchromatic, black-and-white infrared, color, and infrared, 35mm aerial photography, thermal infrared, radar, multispectral scanner, and LANDSAT. Upon completion of the exercises the student is expected to know: (1) the electromagnetic spectrum, its various wavelength sub-sections and their uses as sensors, (2) the limitations of each sensor, (3) the interpretation techniques used for extracting data from the various types of imagery, and (4) the cost effectiveness of remote sensing procedures for acquiring and evaluating data of the natural environment.
Mintzer, O.↗
Spectral estimates of intercepted solar radiation by corn and soybean canopies
Attention is given to the development of methods for combining spectral and meteorological data in crop yield models which are capable of providing accurate estimates of crop condition and yields throughout the growing season. The present investigation is concerned with initial tests of these concepts using spectral and agronomic data acquired in controlled experiments. The data were acquired at the Purdue University Agronomy Farm, 10 km northwest of West Lafayette, Indiana. Data were obtained throughout several growing seasons for corn and soybeans. Five methods or models for predicting yields were examined. On the basis of the obtained results, it is concluded that estimating intercepted solar radiation using spectral data is a viable approach for merging spectral and meteorological data in crop yield models.
Gallo, K. P.↗
An initial model for estimating soybean development stages from spectral data
A model, utilizing a direct relationship between remotely sensed spectral data and soybean development stage, has been proposed. The model is based upon transforming the spectral data in Landsat bands to greenness values over time and relating the area of this curve to soybean development stage. Soybean development stages were estimated from data acquired in 1978 from research plots at the Purdue University Agronomy Farm as well as Landsat data acquired over sample areas of the U.S. Corn Belt in 1978 and 1979. Analysis of spectral data from research plots revealed that the model works well with reasonable variation in planting date, row spacing, and soil background. The R-squared of calculated U.S. observed development stage exceeded 0.91 for all treatment variables. Using Landsat data the calculated U.S. observed development stage gave an R-squared of 0.89 in 1978 and 0.87 in 1979. No difference in the models performance could be detected between early and late planted fields, small and large fields, or high and low yielding fields.
Henderson, K. E.↗
Sun-view angle effects on reflectance factors of corn canopies
The effects of sun and view angles on reflectance factors of corn (Zea mays L.) canopies ranging from the six leaf stage to harvest maturity were studied on the Purdue University Agronomy Farm by a multiband radiometer. The two methods of acquiring spectral data, the truck system and the tower systrem, are described. The analysis of the spectral data is presented in three parts: solar angle effects on reflectance factors viewed at nadir; solar angle effects on reflectance factors viewed at a fixed sun angle; and both sun and view angles effect on reflectance factors. The analysis revealed that for nadir-viewed reflectance factors there is a strong solar angle dependence in all spectral bands for canopies with low leaf area index. Reflectance factors observed from the sun angle at different view azimuth angles showed that the position of the sensor relative to the sun is important in determining angular reflectance characteristics. For both sun and view angles, reflectance factors are maximized when the sensor view direction is towards the sun.
Ranson, K. J.↗
Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat
The use of remote sensing to determine seasonal changes in agronomic and spectral properties of winter wheat canopies with different levels of N fertilization is investigated. Field experiments were conducted at Purdue Agronomy Farm, West Lafayette, IN during the 1978-1979 and 1979-1980 growing season. Spectral reflectance, total leaf N concentration, leaf chlorophyll concentration, leaf are index (LAI), and fresh and dry phytomass are measured and analyzed. Three distinct wheat canopies are detected for the O, 60, and 120 kg N/ha levels of fertilization; it is observed that with an increase in N the reflectance in the visible, and middle IR wavelengths decrease, and the IR reflectance is increased. The canopies with 120 kg N/ha display the highest LAI, maintain green leaf area the longest, and increase in fresh and dry phytomass. The relationship between spectral and agronomic variables is examined; the effect of changing chlorophyll concentration and LAI on the reflectance is studied.
Hinzman, L. D.↗
Observed Impacts
Agricultural applications of El Nino forecasts are already underway in some countries and need to be evaluated or re-evaluated. For example, in Peru, El Nino forecasts have been incorporated into national planning for the agricultural sector, and areas planted with rice and cotton (cotton being the more drought-tolerant crop) are adjusted accordingly. How well are this and other such programs working? Such evaluations will contribute to the governmental and intergovernmental institutions, including the Inter-American Institute for Global Change Research and the US National Ocean and Atmospheric Agency that are fostering programs to aid the effective use of forecasts. This research involves expanding, deepening, and applying the understanding of physical climate to the fields of agronomy and social science; and the reciprocal understanding of crop growth and farm economics to climatology. Delivery of a regional climate forecast with no information about how the climate forecast was derived limits its effectiveness. Explanation of a region's major climate driving forces helps to place a seasonal forecast in context. Then, a useful approach is to show historical responses to previous El Nino events, and projections, with uncertainty intervals, of crop response from dynamic process crop growth models. Regional forecasts should be updated with real-time weather conditions. Since every El Nino event is different, it is important to track, report and advise on each new event as it unfolds.
Rosenzweig, Cynthia↗
Section on Observed Impacts on El Nino
Agricultural applications of El Nino forecasts are already underway in some countries and need to be evaluated or re-evaluated. For example, in Peru, El Nino forecasts have been incorporated into national planning for the agricultural sector, and areas planted with rice and cotton (cotton being the more drought-tolerant crop) are adjusted accordingly. How well are this and other such programs working? Such evaluations will contribute to the governmental and intergovernmental institutions, including the Inter-American Institute for Global Change Research and the US National Ocean and Atmospheric Agency that are fostering programs to aid the effective use of forecasts. As El Nino climate forecasting grows out of the research mode into operational mode, the research focus shifts to include the design of appropriate modes of utilization. Awareness of and sensitivity to the costs of prediction errors also grow. For example, one major forecasting model failed to predict the very large El Nino event of 1997, when Pacific sea-surface temperatures were the highest on record. Although simple correlations between El Nino events and crop yields may be suggestive, more sophisticated work is needed to understand the subtleties of the interplay among the global climate system, regional climate patterns, and local agricultural systems. Honesty about the limitations of an forecast is essential, especially when human livelihoods are at stake. An end-to-end analysis links tools and expertise from the full sequence of ENSO cause-and-effect processes. Representatives from many disciplines are needed to achieve insights, e.g, oceanographers and atmospheric scientists who predict El Nino events, climatologists who drive global climate models with sea-surface temperature predictions, agronomists who translate regional climate connections in to crop yield forecasts, and economists who analyze market adjustments to the vagaries of climate and determine the value of climate forecasts. Methods include historical studies to understand past patterns and to test hindcasts of the prediction tools, crop modeling, spatial analysis and remote sensing. This research involves expanding, deepening, and applying the understanding of physical climate to the fields of agronomy and social science; and the reciprocal understanding of crop growth and farm economics to climatology. Delivery of a regional climate forecast with no information about how the climate forecast was derived limits its effectiveness. Explanation of a region's major climate driving forces helps to place a seasonal forecast in context. Then, a useful approach is to show historical responses to previous El Nino events, and projections, with uncertainty intervals, of crop response from dynamic process crop growth models. Regional ID forecasts should be updated with real-time weather conditions. Since every El Nino event is different, it is important to track, report and advise on each new event as it unfolds. The stability of human enterprises depends on understanding both the potentialities and the limits of predictability. Farmers rely on past experience to anticipate and respond to fluctuations in the biophysical systems on which their livelihoods depend. Now scientists are improving their ability to predict some major elements of climate variability. The improvements in the reliability of El Nino forecasts are encouraging, but seasonal forecasts for agriculture are not, and will probably never be completely infallible, due to the chaotic nature of the climate system. Uncertainties proliferate as we extend beyond Pacific sea-surface temperatures to climate teleconnections and agricultural outcomes. The goal of this research is to shed as a clear light as possible on these inherent uncertainties and thus to contribute to the development of appropriate responses to El Nino and other seasonal forecasts for a range of stakeholders, which, ultimately, includes food consumers everywhere.
Rosenzweig, Cynthia↗
The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science
The Agricultural Model Intercomparison and Improvement Project (AgMIP) was founded in 2010. Its mission is to improve substantially the characterization of world food security as affected by climate variability and change, and to enhance adaptation capacity in both developing and developed countries. The objectives of AgMIP are to: Incorporate state-of-the-art climate, crop/livestock, and agricultural economic model improvements into coordinated multi-model regional and global assessments of future climate impacts and adaptation and other key aspects of the food system. Utilize multiple models, scenarios, locations, crops/livestock, and participants to explore uncertainty and the impact of data and methodological choices. Collaborate with regional experts in agronomy, animal sciences, economics, and climate to build a strong basis for model applications, addressing key climate related questions and sustainable intensification farming systems. Improve scientific and adaptive capacity in modeling for major agricultural regions in the developing and developed world, with a focus on vulnerable regions. Improve agricultural data and enhance data-sharing based on their intercomparison and evaluation using best scientific practices. Develop modeling frameworks to identify and evaluate promising adaptation technologies and policies and to prioritize strategies.
farm crops↗
Preface
The potential effects of climate change on the food production system are raising concern both globally and regionally. The system is already challenged to deliver sufficient and healthy sustenance to all people, and is certain to be even further challenged as world population grows and price shocks loom. The prospect of climate change intensifies these challenges, raising the risk that more frequent and intense extreme weather events threaten the stability of agricultural production in regions around the globe. This two-part set is an important contribution to the ongoing Imperial College Press (ICP) Series on Climate Change Impacts, Adaptation, and Mitigation. This series aims to provide the know ledge base necessary for understanding and responding to climate change, in both its current form and future manifestations. In these volumes, ·leading agricultural researchers have come together to contribute their expertise on actual and potential climate change impacts, adaptation strategies, and mitigation efforts. This ongoing series is jointly published by The American Society of Agronomy (ASA), Crop Science Society of America (CSSA), and Soil Science Society of America (SSSA), together with ICP. We hope that this fruitful cooperation will continue for many years to come, as it spurs the global effort to define and meet the great food security and climate change challenges of our time.
farmlands↗