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United States Department of Agriculture

Agricultural Research Service

Title: Metrics of Climate Change Impact and Adaptation of Physiologically-diverse Crops)

item Jaradat, Abdullah

Submitted to: Meeting Abstract
Publication Type: Abstract only
Publication Acceptance Date: 3/12/2009
Publication Date: 3/12/2009
Citation: Jaradat, A.A. 2009. Metrics of Climate Change Impact and Adaptation of Physiologically-diverse Crops [abstract]. IOP Conference Series: Earth and Environmental Science. Paper No. 372021.

Interpretive Summary:

Technical Abstract: Climate change is predicted to be most pronounced at high latitudes and is expected to cause a net loss of agro-ecosystem carbon (C) and a positive feedback to global warming. Nevertheless, demand for food, feed, fiber and bioenergy continues to be a major driver of global climate change, especially in the cropping systems of the upper Midwest (UMW) of the USA. Precipitation, temperature and length of the growing season (measured in growing degree days (GDD)) are expected to impact agriculture in these high-production cropping systems in which average farm yields ~70% of yield potential presently. Significant scientific advances, critical to understand and quantify response indicators of crop plants to climate and management stresses, are crucial in formulating regulatory, technological, and policy changes needed to successfully mitigate the contribution of agriculture to climate change and to adapt agriculture to climate change. The objectives of a series of long-term studies were to quantify the impact of climate change on (1) plant architecture, seed characteristics and yield interrelationships; and on (2) dry matter (DM) partitioning and the C:Nitrogen:Phosphorus (C:N:P) ratio in the mature seed and its impact on yield. Additionally, measured and simulated data were used to (3) predict the climate-adjusted genetic yield potential ceiling for carbohydrates-, protein-, and oil-producing crops; and to (4) develop a model-based risk assessment of crop production under the most likely climate change scenarios by 2058. Multi-year fractional factorial experiments were conducted to study the impact of multiple climatic variables and management factors on corn (Zea mays L.); sweet sorghum [Sorghum bicolor (L.) Moench]; soybean [Glycine max (L.) Merr.]; wheat (Triticum aestivum L. and Triticum durum L.); chickpea (Cicer arietinum L.); safflower (Cartahmus tinctorius L.); and Cuphea spp. (Cuphea viscosissima x C. lanceolata), a semi-domesticated oil-producing bioenergy crop in the UMW (45º 41' N, 95º 48' W, elevation 370 m). Multi-year data were collected from greenhouse experiments and from permanent geo-referenced sampling sites established within long-term field experiments on soil (physical and chemical properties), and plant variables (phenotypic plasticity, allocation of DM, C, N, and P to roots, stems, leaves, and seed of two contrasting genotypes of each crop species). Current and simulated weather variables [temperature, solar irradiance, rainfall, and actual (ET) and potential (PET) evapo-transpiration] were used in conjunction with current (380 ppm; 2008) and projected (~800 ppm; 2058) levels of carbon dioxide in simulating crop response to likely climate change scenarios. Yield potential under the relatively short growing season in the UMW was determined for each crop in the field by the amount of incident solar radiation, temperature, and population density. Dry matter, C, and N partitioned to plant organs and to the mature seed under no-stress were estimated under controlled conditions in growth chambers and used as a baseline for comparison with their respective estimates under stress conditions and simulation results. Results of empirical analyses [fractal dimension (Do) of plant architecture, partial least squares (PLS) regression, and restricted maximum likelihood (REML) for variance components estimation] and modeling [feed-forward, back-propagation Artificial Neural Networks (ANN)] studies indicated large inter- and intra-species contrasts in response to single and multiple, interacting stress factors and their impact on seed characteristics (e.g., weight and C:N:P ratio) and grain yield. The variance in DM, C, N, and P partitioned among and within species, and among organs within species (VAR) indicated that crops differed as to their adaptive capacity to likely climate change scenarios, and that VAR plays a critic

Last Modified: 8/24/2016
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