Calvin D. Perry

Water deficit in field-grown Gossypium hirsutum primarily limits net photosynthesis by decreasing stomatal conductance, increasing photorespiration, and increasing the ratio of dark respiration to gross photosynthesis

Much effort has been expended to improve irrigation efficiency and drought tolerance of agronomic crops; however, a clear understanding of the physiological mechanisms that interact to decrease source strength and drive yield loss has not been attained. To elucidate the underlying mechanisms contributing to inhibition of net carbon assimilation under drought stress, three cultivars of Gossypium hirsutum were grown in the field under contrasting irrigation regimes during the 2012 and 2013 growing season near Camilla, Georgia, USA. Physiological measurements were conducted on three sample dates during each growing season (providing a broad range of plant water status) and included, predawn and midday leaf water potential (ΨPD and ΨMD), gross and net photosynthesis, dark respiration, photorespiration, and chlorophyll a fluorescence. End-of-season lint yield was also determined. ΨPD ranged from -0.31 to -0.95MPa, and ΨMD ranged from -1.02 to -2.67MPa, depending upon irrigation regime and sample date. G. hirsutum responded to water deficit by decreasing stomatal conductance, increasing photorespiration, and increasing the ratio of dark respiration to gross photosynthesis, thereby limiting PN and decreasing lint yield (lint yield declines observed during the 2012 growing season only). Conversely, even extreme water deficit, causing a 54% decline in PN, did not negatively affect actual quantum yield, maximum quantum yield, or photosynthetic electron transport. It is concluded that PN is primarily limited in drought-stressed G. hirsutum by decreased stomatal conductance, along with increases in respiratory and photorespiratory carbon losses, not inhibition or down-regulation of electron transport through photosystem II. It is further concluded that ΨPD is a reliable indicator of drought stress and the need for irrigation in field-grown cotton.

Adapting the CROPGRO-Cotton model to simulate cotton biomass and yield under southern root-knot nematode parasitism.

Cotton (Gossypium hirsutum L.) yield losses by southern root-knot nematode (RKN; Meloidogyne incognita (Kofoid & White) Chitwood) are usually assessed after significant damage has been caused. However, estimation of potential yield reduction before planting is possible by using crop simulation. The main goal of this study was to adapt the Cropping System Model (CSM)-CROPGRO-Cotton for simulating growth and yield of cotton plants infected with RKN. Two hypotheses were evaluated to simulate RKN damage: (1) RKN acting as a sink for soluble assimilate, and (2) RKN inducing a reduction of root length per root mass and root density. The model was calibrated and adapted using data collected in an experiment that was conducted in 2007 and was part of a long-term crop rotation study. The experiment had a split-plot design, replicated six times, with drought stress levels assigned to the main plots and fumigation levels assigned to the subplots. The model was evaluated with seed cotton weight data collected in an experiment that was conducted in 2001 and was part of the same long-term crop rotation experiment. The fumigation treatments created various levels of RKN population densities. The model was adapted by coupling the RKN population to the removal of daily assimilates and decreasing root length per unit mass. The assimilate consumption rate was obtained after minimizing the error between simulated and observed biomass and yield components for the limited drought stress, non-fumigated treatment. Different values of root length per unit root weight (RFAC1) were used to account for early symptoms of RKN damage on leaf area index (LAI) and vegetative biomass under the non-fumigated, drought stress conditions. After model adaptation, the simulations indicated that LAI, total biomass, boll weight, and seed cotton decreased with elevated RKN population. The impact of RKN was more pronounced under severe drought stress. The lowest RMSE of LAI simulations occurred for the non-fumigated treatments under medium and severe drought stress (0.71 and 0.65 m2 m-2, respectively). Biomass was simulated with a prediction error within a range of 6% to 18.4% and seed cotton within a range of -11.2% to 2.7%. Seed cotton weight losses associated with RKN infection increased with the level of drought stress (9%, 20%, and 18% for the low, medium, and severe drought stress). Model evaluation showed that seed cotton weight was slightly more overpredicted for the fumigated than for the non-fumigated treatments, with prediction errors of 28.2%, 15.8%, and 2.0% for the low, medium, and severe drought stress, respectively. Similar to the calibration of the model, the yield losses increased with the combination of RKN and drought stress (20% and 29% for the low and severe drought stress). The results showed the potential for using the CSM-CROPGRO-Cotton model to account for RKN damage as well as to simulate yield reduction. However, further model evaluation might be needed to evaluate the values of assimilate consumption and root length per unit weight for different environmental conditions and management practices.

Effects of Variable-Rate Sprinkler Cycling on Irrigation Uniformity

Field studies with a center pivot (CP) and lateral-move (LM) irrigation systems were conducted to evaluate whether cycling of sprinklers ON and OFF to achieve variable application rates affects the overall application uniformity of an irrigation system. For the CP tests, collector test results showed CUC and DUlq values were 89 or greater with a slight degradation as the sprinkler cycling rate decreased. For the LM tests, the CUC and DUlq values were not as high as for the CP, with values that ranged from 50 to 92, but no effect of sprinkler cycling rate on uniformity was observed. Overall, sprinkler cycling ON and OFF appears to have no discernable impact on overall CP or LM application uniformity. Thus, this method of creating a variable application rate in zones along a CP or LM mainline should not cause unintended degradation of the system’s sprinkler uniformity.

Predicting Cotton Lint Yield Maps from Aerial Photographs

It is generally accepted that aerial images of growing crops provide spatial and temporal information about crop growth conditions and may even be indicative of crop yield. The focus of this study was to develop a straightforward technique for creating predictive cotton yield maps from aerial images. A total of ten fields in southern Georgia, USA, were studied during three growing seasons. Conventional (true color) aerial photographs of the fields were acquired during the growing season in two to four week intervals. The aerial photos were then digitized and analyzed using an unsupervised classification function of image analysis software. During harvest, conventional yield maps were created for each of the fields using a cotton picker mounted yield monitor. Classified images and yield maps were compared quantitatively and qualitatively. A pixel by pixel comparison of the classified images and yield maps showed that spatial agreement between the two gradually increased in the weeks after planting, maintained spatial agreement of between 40% and 60% during weeks eight to fourteen, and then gradually declined again. The highest spatial agreement between a classified image and a yield map was 78%. The highest average agreement was 52% and occurred 9.9 weeks after planting. The visual similarity between the classified images and the yield maps were striking. In all cases, the dates with the best visual agreement occurred between eight and ten weeks after planting, and generally, during July for southern Georgia. This method offers great potential for offering cotton farmers early-season maps that predict the spatial distribution of yield. Although these maps can not provide magnitudes, they clearly show the resulting yield patterns. With inherent knowledge of past performance, farmers can use this information to allocate resources, address crop growth problems, and, perhaps, improve the profitability of their farm operation. These maps are well suited to be offered to farmers as a service by a crop consultant or a cooperative.

THE PEANUT YIELD MONITORING SYSTEM

The most essential component of precision farming is the yield monitor, a sensor or group of sensors installed on harvesting equipment that dynamically measure spatial yield variability. Yield maps, which are produced using data from yield monitors, are extremely useful in providing the farmer a color–coded visual image clearly showing the variability of yield across a field. University of Georgia scientists recently completed development work on PYMS, the Peanut Yield Monitoring System. PYMS uses load cells for instantaneous load measurements of harvested peanuts and has proven to be accurate to between 2% and 3% on a trailer–load basis and to approximately 1% on a field basis when using data collected during combine operation. PYMS data are accurate to around 1% on a basket–load basis when using data collected under static conditions. The instantaneous accuracy of PYMS was calculated to be 700 kg/ha. Basing management decisions on the yield of individual pixels of PYMS yield maps is not realistic. The strength of PYMS is in differentiating yield trends and evaluating management practices. The system was extensively and successfully field–tested over a 3–year period and evaluated by 11 users during 1999, all of whom were able to use the resulting yield maps to evaluate current management practices or to develop future management plans. The University of Georgia has submitted a patent application for PYMS, and the technology has been licensed.

Leaf ontogeny strongly influences photosynthetic tolerance to drought and high temperature in Gossypium hirsutum

Temperature and drought are major abiotic limitations to crop productivity worldwide. While abiotic stress physiology research has focused primarily on fully expanded leaves, no studies have investigated photosynthetic tolerance to concurrent drought and high temperature during leaf ontogeny. To address this, Gossypium hirsutum plants were exposed to five irrigation treatments, and two different leaf stages were sampled on three dates during an abnormally dry summer. Early in the growing season, ontogenic PSII heat tolerance differences were observed. Photosystem II was more thermotolerant in young leaves than mature leaves. Later in the growing season, no decline in young leaf net photosynthesis (PN) was observed as leaf temperature increased from 31 to 37°C, as average midday leaf water potential (ΨMD) declined from -1.25 to -2.03MPa. In contrast, mature leaf PN declined 66% under the same conditions. Stomatal conductance (gs) accounted for 84-98% of variability in leaf temperature, and gs was strongly associated with ΨMD in mature leaves but not in young leaves. We conclude that young leaves are more photosynthetically tolerant to heat and drought than mature leaves. Elucidating the mechanisms causing these ontogenic differences will likely help mitigate the negative impacts of abiotic stress in the future.

A soil moisture sensor-based variable rate irrigation scheduling system

To assess the potential of precision irrigation, a research and demonstration project whose goal is to develop a soil moisture sensor-based variable rate irrigation (VRI) control system was begun. The control system consists of a wireless soil moisture sensing array with a high density of sensor nodes, a VRI enabled center pivot irrigation system, and a web-based user interface with an integrated irrigation scheduling decision support system. This paper describes the system in detail providing some results from the components which have been completed and are operational and a detailed description of the components under development.

DECONVOLUTION OF SITE-SPECIFIC YIELD MEASUREMENTS TO ADDRESS PEANUT COMBINE DYNAMICS

During the development of a peanut yield monitoring system, experiments were conducted on a two-row peanut combine to determine the duration of time lag between pickup and yield measurement, and to characterize the convolution of peanut flow within the combine. The research indicates that the two-row peanut combine used in the experiment subjects harvested product to significant convolution. A simple time lag correction will not recover the site specific (short term accuracy) of yield measurements. The distance and time period required to achieve a yield estimate error less than 20% (95% confidence) is greater than 19.7 m (17 s) for simple time lag correction while it is 5.8 m (5 s) for deconvoluted data. The net result is that smaller regions of yield variability may be recognized with greater confidence using the deconvolution method than with the simple time delay method.

Development and assessment of a smartphone application for irrigation scheduling in cotton

Easy-to-use and engaging smartphone application.Interactive ET-based soil water balance model.Uses meteorological data from weather station networks.Estimates root zone soil water deficits (RZSWD).Has mostly outperformed other irrigation scheduling tools. The goal of this work was to develop an easy-to-use and engaging irrigation scheduling tool for cotton which operates on a smartphone platform. The model which drives the Cotton SmartIrrigation App (Cotton App) is an interactive ET-based soil water balance model. The Cotton App uses meteorological data from weather station networks, soil parameters, crop phenology, crop coefficients, and irrigation applications to estimate root zone soil water deficits (RZSWD) in terms of percent as well as of inches of water. The Cotton App sends notifications to the user when the RZSWD exceeds 40%, when phenological changes occur, and when rain is recorded at the nearest weather station. It operates on both iOS and Android operating systems and was released during March 2014. The soil water balance model was calibrated and validated during 2012 and 2013 using data from replicated plot experiments and commercial fields. The Cotton App was evaluated in field trials for three years and performed well when compared to other irrigation scheduling tools. Its geographical footprint is currently limited to the states of Georgia and Florida, United States, because it is enabled to use meteorological data only from weather station networks in these states. A new version is currently under development which will use national gridded meteorological data sets and allow the Cotton App to be used in most cotton growing areas of the United States.

Predawn respiration rates during flowering are highly predictive of yield response in Gossypium hirsutum when yield variability is water-induced

Respiratory carbon evolution by leaves under abiotic stress is implicated as a major limitation to crop productivity; however, respiration rates of fully expanded leaves are positively associated with plant growth rates. Given the substantial sensitivity of plant growth to drought, it was hypothesized that predawn respiration rates (RPD) would be (1) more sensitive to drought than photosynthetic processes and (2) highly predictive of water-induced yield variability in Gossypium hirsutum. Two studies (at Tifton and Camilla Georgia) addressed these hypotheses. At Tifton, drought was imposed beginning at the onset of flowering (first flower) and continuing for three weeks (peak bloom) followed by a recovery period, and predawn water potential (ΨPD), RPD, net photosynthesis (AN) and maximum quantum yield of photosystem II (Fv/Fm) were measured throughout the study period. At Camilla, plants were exposed to five different irrigation regimes throughout the growing season, and average ΨPD and RPD were determined between first flower and peak bloom for all treatments. For both sites, fiber yield was assessed at crop maturity. The relationships between ΨPD, RPD and yield were assessed via non-linear regression. It was concluded for field-grown G. hirsutum that (1) RPD is exceptionally sensitive to progressive drought (more so than AN or Fv/Fm) and (2) average RPD from first flower to peak bloom is highly predictive of water-induced yield variability.