CO2 and H2O exchange of crop ecosystems in the Southeast

Summary

In 2018 we conducted (1) experiments and study of the influence of different peanut planting patterns (single-row vs. twin-row) on its water-use efficiency and yield, (2) experiments aimed at helping peanut growers to optimize their peanut planting dates, (3) experiments in a pecan orchard to develop a model for irrigation and water-use efficiency management in pecan tree growth, (4) a study using an economical micro-drone equipped with a camera sensitive to water content changes in the golf turf to develop a low-cost irrigation scheduling methodology, and (5) determine the amount of carbon dioxide stored by Spartina Alterniflora L. one of the most productive ecosystems on earth on the Georgia Coast near Sapelo Island.

Situation

Peanut is an important crop in the southeastern US, especially in Georgia. The frequency of both water stress and drought associated with climate variability is on the rise. It is thus becoming increasingly critical to improve water-use efficiency while maintaining high peanut yields through different management practices. The peanut experiments in 2018 were conducted to indicate how different planting patterns (single row vs. twin row) affect peanut water-use efficiency and yield at the field scale. This is to help growers optimize their planting pattern to conserve water while getting high yield. With different planting dates, the development stages of peanut occur in different weather and other environmental conditions that affect peanut growth and its water-use efficiency. Farmers can greatly benefit from using a more quantitative measure of the optimum planting date each year. With water conservation rapidly becoming a source of concern in the state, conservation of water resources is quickly becoming a priority for golf course superintendents. While golf courses have historically been proactive in addressing efficient irrigation, increasingly limited water resources require a more quantitative approach to determine the degree of water stress so that the optimum amount of water is applied on golf courses. In 2018, we used a low-cost drone technology hosting a multi-spectral camera to assess the optimum amount of water used by turf in golf courses and the spatial distribution of water stress, in an effort to seek the optimum irrigation schedule by identifying the water deficit. Pecan trees represent an economically important crop in the state of Georgia. A climate increasingly drier combined with growing pressure on water resources fuels the need to optimize existing resources to maximize yield, a foremost concern for growers. This emerging reality needs to be balanced against the fact that pecan trees are highly sensitive to even the slightest water stress, perhaps more so than any other crop. These two opposing forces points to the need of judicious planning of water allocation. The present work measures a vast of variables not previously measured in the southeast. This is a necessary requirement to accurately predict optimum water use needed along with the predicted yield. The model, well tested and widely used in the West, needs a systematic comprehensive database of pecan characteristics and environmental factors in the Southeast to verify and adapt a pecan nut tree growth and water-use model for micro irrigated hedged pecan orchards in Georgia. A little understood ecosystem in the state is the coastal one. Salt marshes are among the most productive and dynamic ecosystems on Earth and globally sequester an average of 210?g?C?m?2?yr?1. To understand the role of this ecosystem in the carbon cycle and its changes as a result of rapid climate change and human disturbance, a baseline record particularly on carbon dioxide (CO2) exchange between this ecosystem and atmosphere needs to be established. The goal of this study is to determine the effects tide events on the exchange of CO2 in a salt marsh ecosystem dominated by Spartina alterniflora using the eddy-covariance method near Sapelo Island, GA.

Response

1. Peanut crop with different planting patterns In 2018, we conducted experiments in two adjacent, large, flat, irrigated peanut fields at the Southwest Research and Education Center in Plains, GA. In two of the fields peanuts were planted on the same date, with single-row planting pattern in one field and twin-row in another field. Peanut cultivar Georgia-O6G was used. Evapotranspiration, carbon dioxide exchanges with the atmosphere, and water use efficiency in each field were measured and calculated with the eddy-covariance method. Meanwhile, soil CO2, soil temperature and soil water content were also simultaneously monitored in each field. Leaf area index in each field was measured at weekly intervals. At about 90 days after planting date when peanut is usually at the peak of crop physiological performance, peanut leaf and peg samples were collected at five locations in each field to analyze through mass spectrometry for carbon isotope ration of peanut leaf and peg tissues. These measurements will help compare the integrated measure of the seasonal peanut water-use efficiency between single- and twin-row planting patterns and among different planting dates. Peanut yield sampling and harvesting from each field were taken when peanut became mature to measure peanut pod number per plant, peanut size, peanut weight, and yield. All the information would help explain the difference in peanut water-use efficiency between the different planting patterns and among the different planting dates. 2. Peanut crop with different planting dates In the other two fields adjacent to above-stated peanut fields, peanuts were planted in single-row pattern with one field on an earlier date and another field on a later date. Together with the above-stated planting date, there are total three planting dates with peanuts planted in single-row pattern. Peanut cultivar Georgia-O6G was also used. Evapotranspiration, carbon dioxide exchange with the atmosphere, and water-use efficiency, soil CO2, soil temperature, soil water content, leaf area index, peanut leaf and peg samples for carbon isotopic analysis, peanut pod number per plant, peanut size, peanut weight, and yield were also measured with the methods as the same as the experiments with different planting patterns. 3. Optimum Scheduling of Water Application on Golf Courses in the State In 2018, we identified golf courses that were candidates for the second year of the study. The golf course used in 2017 was sold so we redirected our efforts at one of our earlier candidate locations at the Southern Hills Golf Course in mid-Georgia. The study began with the assessment of the microclimatology which varies considerably from location to location within the golf course. It was then followed by testing the drone/camera system and the area encompassed by the system. The optimum altitude and the steadiness of flight legs were assessed. Biophysical measurements were also made simultaneously within the flight region. These included leaf temperature, soil moisture at two levels, leaf water content and leaf water potential. Following these tests, an eddy-covariance flux system was installed at the site to get the flux data of evapotranspiration from the fairway in front of it. We flew the drone over the fairway and got images from the multispectral camera mounted on the drone. Multi-spectral images of golf courses derived from both the drone-mounted spectral camera are being processed into vegetation index maps and correlated with golf course evapotranspiration data from eddy-covariance measurements. These are used as an indicator of both water stress and drought for irrigation scheduling. The relationship between the multispectral image outputs and golf course evapotranspiration is established to guide irrigation scheduling. The collected data is available from these experiments and related to eddy-covariance measurements and with the other above-mentioned biophysical variables in the field. 4. Salt marsh Spartina In 2018, we analyzed the eddy-covariance data to determine the effects tide events on the exchange of CO2 in a salt marsh ecosystem dominated by Spartina alterniflora near Sapelo Island, GA. The data were collected using the eddy-covariance method from July 2013 when two eddy-covariance systems were set up to end of 2016. A peer-review paper was published in 2017 and another paper is in preparation.

Impact

1. Peanut crop with different planting patterns Peanut eddy-covariance data of different panting patterns during the period before peanut canopy fully covered the ground have been processed and evapotranspiration, CO2 fluxes, and water-use efficiency have been calculated and analyzed. Preliminary results suggest that twin-row peanut has better water-use efficiency than single-row peanut before the peanut canopy is fully covered. Processing and analyzing all data for the third year of the study in the growth season are being conducted. 2. Peanut crop with different planting dates The data during the growing season corresponding to each of three different planting dates are being processed. Different formulations of growing-degree days as a mean of intercomparing the three planting dates in the light of year-to-year climate variability are being tested. 3. Salt marsh Spartina We found that during daytime high tide events, a reduction of CO2 exchange was observed. The conditions with a large ratio of tide height to vegetation height are associated with smaller CO2 exchange when compared to conditions with a small ratio. Total daytime monthly reduction of CO2 exchange for August 2014 was 15?%. A greater total reduction of CO2 exchange of 40?% was recorded for high tide events with tide ratio of 0.75–1.0. In comparison of the effect of neap tide and spring tide on CO2 exchange, neap tide days showed a greater CO2 exchange as compared to spring tide days for May and October 2014, respectively. The inclusion of such results has implications to quantify the carbon budget and its changes as sea level rises

State Issue

Sustainability, Conservation, & the Environment

Details

• Year: 2018
• Geographic Scope: Multi-State/Regional
• County: Spalding
• Program Areas:
  • Agriculture & Natural Resources