Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models

One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties

Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models

One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (V-c,V-max) and maximum electron transport rate supporting RuBP regeneration (J(max)), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate V-c,V-max and J(max) based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for V-c,V-max (R-2 = 0.70) and J(max) (R-2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral propertiesThe authors would like to thank the technical help during the experiment of Mr. Robert Icenogle, Barry Dorman (USDA-ARS), Seth Johnston, and Mary Durstock (Crop Physiology Laboratory, Auburn University). The authors also would like to thank to Dr. Jose A. Jimenez Berni for statistical support to analyze the data. This research was supported by the Action CA17134 SENSECO (Optical Synergies for Spatiotemporal Sensing of Scalable Ecophysiological Traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu).This research was also supported by Auburn University and Alabama Agricultural Experimental Station Seed Grant

Effects of elevated [CO2] on photosynthesis and seed yield parameters in two soybean genotypes with contrasting water use efficiency

The predicted increase in atmospheric CO concentration [CO] is expected to enhance photosynthesis and seed yield in crops such as soybean [Glycine max (L.) Merr.]. However, future breeding for high water use efficiency (WUE) could interfere with the amount of carbon (C) fixed by leaves and seed mineral composition under elevated [CO] due to lower stomatal conductance (g). In the present study, two genotypes with contrasting WUE were grown in open top chambers (OTC) under ambient (410 ppm; a[CO]) and elevated (610 ppm; e[CO]). In order to test performance of both cultivars to changing CO conditions, growth, photosynthetic performance (leaf and canopy level) and seed mineral composition were analyzed. The low WUE genotype had a greater response to e[CO] in terms of leaf daily photosynthetic C gain due to greater g, which was compensated in the high WUE genotype by an increase in leaf area (LA). However, in the low WUE genotype, improved daily photosynthetic C gain did not translate into greater biomass or seed yield [CO] response compared to the high WUE genotype, suggesting better assimilate partitioning by the high WUE genotype. In terms of seed composition, the high WUE genotype generally had lower mineral concentrations at e[CO] compared to a[CO], but greater total amounts of nutrient (due to higher seed yield) under e[CO] compared to the low WUE genotype. Findings presented here highlight importance of genetic variation in soybean response to future atmospheric [CO] which should be considered when breeding for future climates.David Soba is the recipient of a PhD grant supported by the Public University of Navarra, and was the recipient of a travel grant from the same institution. This project was partially funded by the Alabama Soybean Farmers Association, the Alabama agricultural experiment station and the Hatch program of the National Institute of Food and agriculture, U.S. department of Agriculture

Greenhouse Gas Emissions in the Process of Landfill Disposal in China

Quantitative accounting of greenhouse gas (GHG) emissions has become an important global focus. GHG emissions from the waste sector have high potential in GHG emissions reduction. We analyzed the GHG emissions inventory in the waste sector of the European Union, Germany, the United Kingdom, the United States of America, and Canada from 1990 to 2019. Landfill disposal was the main category of GHGs from the waste sector, with a contribution rate between 69% and 95%. Landfill disposal also played a prominent role in emission reduction, with a contribution rate higher than 86%. GHG emissions from landfill sites in China were calculated using the inventory analysis method recommended by the IPCC and combined with actual situations. The results showed that the highest GHG emissions from landfill disposal in China occurred in 2020, with an estimated 165 million tons of carbon dioxide (CO2) equivalent. In 2019, the per capita GHG emissions from landfill sites in China was 117 kg CO2 equivalent/person, which was higher than Germany (87 kg CO2 equivalent/person) but lower than the European Union (189 kg CO2 equivalent/person)