A. y Garcia

Effect of high temperature on photosynthesis and transpiration of sweet corn (Zea mays L. var. rugosa)

Four temperature treatments were studied in the climate controlled growth chambers of the Georgia Envirotron: 25/20, 30/25, 35/30, and 40/35 °C during 14/10 h light/dark cycle. For the first growth stage (V3-5), the highest net photosynthetic rate (PN) of sweet corn was found for the lowest temperature of 28–34 µmol m−2 s−1 while the PN for the highest temperature treatment was 50–60 % lower. We detected a gradual decline of about 1 PN unit per 1 °C increase in temperature. Maximum transpiration rate (E) fluctuated between 0.36 and 0.54 mm h−1 (≈5.0–6.5 mm d−1) for the high temperature treatment and the minimum E fluctuated between 0.25 and 0.36 mm h−1 (≈3.5–5.0 mm d−1) for the low temperature treatment. Cumulative CO2 fixation of the 40/35 °C treatment was 33.7 g m−2 d−1 and it increased by about 50 % as temperature declined. The corresponding water use efficiency (WUE) decreased from 14 to 5 g(CO2) kg−1(H2O) for the lowest and highest temperature treatments, respectively. Three main factors affected WUE, PN, and E of Zea: the high temperature which reduced PN, vapor pressure deficit (VPD) that was directly related to E but did not affect PN, and quasi stem conductance (QC) that was directly related to PN but did not affect E. As a result, WUE of the 25/20 °C temperature treatment was almost three times larger than that of 40/35 °C temperature treatment.

Analysis of the Inter-Annual Variation of Peanut Yield in Georgia Using a Dynamic Crop Simulation Model

It is common practice to use crop simulation models and long-term weather data to study the impact of climate variability on yield. Simulated yields mainly reflect the weather variability but not the adoption of new technologies; both sources of variation are reflected in long-term observed yields. Therefore, long-term observed yields, if available, cannot be readily used for evaluation of crop models. The objectives of this study were to analyze the impact of climate variability on long-term historical peanut yield in Georgia obtained with a dynamic crop simulation model and to assess the applicability of using long-term average county yield determined from statistical estimates for evaluation of the simulated yield. Observed yields obtained from state variety trials as well as yield estimates from the USDA-NASS for three counties in the Georgia peanut belt from 1934 to 2003 were used for evaluating simulated yield series. Simulated yields based on the CSM-CROPGRO-Peanut model were categorized into three technological periods (TP). A weighted average based on the acreage of the soil type, the peanut type, and the irrigated land in each county was calculated to obtain a unique simulated yield. Then yields and weather data of the 70-year period were grouped with respect to El Nino Southern Oscillation phases and TPs. Pearsons coefficient of correlation, the least significant difference (LSD), and the t-test were used to evaluate the results. When compared with observed yields, NASS estimates failed to estimate the weather variability at the beginning of the period, but simulated yields clearly reflected that variability during the 70-year period. NASS yield estimates seemed to be useful for evaluating simulated yields from the mid-1970s. The results showed that crop models can be useful in understanding the inter-annual variation of yield due to climate variability if appropriate adjustments are made to account for changes and improvements in agrotechnology.

Reduction in greenhouse gas emissions due to the use of bio-ethanol from wheat grain and straw produced in the south-eastern USA.

Biofuels can reduce greenhouse gas (GHG) emissions by replacing fossil fuels. However, the energy yield from agronomic crops varies due to local climate, weather and soil variability. A variation in the yield of raw material used (feedstock) could also cause variability in GHG reductions if biofuels are used. The goal of the present study was to determine the net reduction of GHG emissions if ethanol from wheat produced in different regions of the south-eastern USA is used as an alternative to gasoline from fossil fuel sources. Two scenarios were investigated; the first included ethanol produced from grain only, and the second included ethanol produced from both grain and wheat straw. Winter wheat yield was simulated with the Cropping System Model (CSM)-CERES-Wheat model for climate, soil and crop management representing six counties in the following USA states: Alabama, Florida and Georgia. Ethanol production was determined from the simulated grain and straw yields together with fixed grain and straw yield ethanol ratios. Subsequently, net reductions in GHG emissions were determined by accounting for the emissions from the replaced gasoline, and by animal feed and electricity that were replaced by ethanol processing co-products. Greenhouse gases that were emitted in the ethanol production chain were also taken into account. Across all locations, the reduction in GHG emissions was 187 g CO 2 -equivalents/km in the grain-only scenario and 208 g CO 2 -equivalents/km in the grain and straw scenario. The reductions in GHG emissions varied significantly between locations and growing seasons within the two scenarios. Similar approaches could be applied to assess the environmental impact of GHG emissions from other biofuels.