Brenda V. Ortiz

Warming up to climate change: a participatory approach to engaging with agricultural stakeholders in the Southeast US

Within the context of a changing climate, scientists are called to engage directly with agricultural stakeholders for the coproduction of relevant information that will support decision making and adaptation. However, values, beliefs, identities, goals, and social networks shape perceptions and actions about climate change. Engagement processes that ignore the socio-cultural context within which stakeholders are embedded may fail to guide adaptive responses. To facilitate dialog around these issues, the Southeast Climate Consortium and the Florida Climate Institute formed a climate learning network consisting of row crop farmers, agricultural extension specialists, researchers, and climate scientists working in the Southeast US. Regional in scope, the learning network engages researchers and practitioners from Alabama, Georgia, and Florida as partners in adaptation science. This paper describes the ongoing interactions, dialog, and experiential learning among the network’s diverse participants. We illustrate how participatory tools have been used in a series of workshops to create interactive spaces for knowledge coproduction. For example, historical timelines, climate scenarios, and technology exchanges stimulated discussions about climate-related risk management. We present findings from the workshops related to participants’ perspectives on climate change and adaptation. Finally, we discuss lessons learned that may be applicable to other groups involved in climate education, communication, and stakeholder engagement. We suggest that the thoughtful design of stakeholder engagement processes can become a powerful social tool for improving decision support and strengthening adaptive capacity within rural communities.

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.

Site-Specific Detection and Management of Nematodes

Nematode distribution varies significantly throughout a field and is highly correlated to soil texture and other edaphic factors. Field-wide application results in nematicides being applied to areas without nematodes and the application of sub-effective levels in areas with high nematode densities. Efforts to use grid maps as a guide to site-specific application have proven to be too expensive to be cost effective. Recently, the availability of GPS –GIS has allowed the use of soil electrical conductivity systems to rapidly and inexpensively develop cost effective soil texture maps. These maps are used to project where nematodes are likely to occur within a field. Variable-rate application systems for granular and fumigant nematicides have been developed and tied via software to soil texture maps providing a mechanism for the effective delivery of nematicides in a site-specific , variable-rate manner in individual fields. Efforts in South Carolina, Georgia, and Arkansas are further developing this system and refining our knowledge of how soil texture and other edaphic factors affect the distribution of cotton nematodes .

Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US

Crop models are one of the most commonly used tools to assess the impact of climate variability and change on crop production. However, before the impact of projected climate changes on crop production can be addressed, a necessary first step is the assessment of the inherent uncertainty and limitations of the forcing data used in these crop models. In this paper, we evaluate the simulated crop production using separate crop models for maize (summer crop) and wheat (winter crop) over six different locations in the Southeastern United States forced with multiple sources of actual and simulated weather data. The paper compares the crop production simulated by a crop model for maize and wheat during a historical period, using daily weather data from three sources: station observations, dynamically downscaled global reanalysis, and dynamically downscaled historical climate model simulations from two global circulation models (GCMs). The same regional climate model is used to downscale the global reanalysis and both global circulation models’ historical simulation. The average simulated yield derived from bias-corrected downscaled reanalysis or bias-corrected downscaled GCMs were, in most cases, not statistically different from observations. Statistical differences of the average yields, generated from observed or downscaled GCM weather, were found in some locations under rainfed and irrigated scenarios, and more frequently in winter (wheat) than in summer (maize). The inter-annual variance of simulated crop yield using GCM downscaled data was frequently overestimated, especially in summer. An analysis of the bias-corrected climate data showed that despite the agreement between the modeled and the observed means of temperatures, solar radiation, and precipitation, their intra-seasonal variances were often significantly different from observations. Therefore, due to this high intra-seasonal variability, a cautious approach is required when using climate model data for historical yield analysis and future climate change impact assessments.

Insect Damage, Aflatoxin Content, and Yield of Bt Corn in Alabama

ABSTRACT Isoline pairs of hybrid corn, similar except for presence or absence of a Bt trait, were planted at eight sites across Alabama over three years. This study evaluated insect damage, yield, and aflatoxin levels as affected by the Bt traits, YieldGard Corn Borer (expressing Cry1Ab), Herculex I (expressing Cry1F), Genuity VT Triple PRO (expressing Cry1A.105 and Cry2Ab2), Agrisure Viptera 3111 (expressing Vip3Aa20 and Cry1Ab), and Genuity SmartStax (expressing Cry1A.105, Cry2Ab2, and Cry1F). When examined over all sites and years, hybrids with any of the included Bt traits had lower insect damage and higher yields. However, insect damage was not consistently correlated to yield. Bt traits expressing multiple proteins provided greater protection from corn earworm feeding than did traits for single proteins. Yields and aflatoxin levels were highly variable among sites although irrigated sites had higher yields than nonirrigated sites. Aflatoxins commonly accumulate in corn in the southeastern United States because of prevailing high temperatures and frequent dry conditions. Aflatoxin levels were not consistently associated with any factors that were evaluated, including Bt traits.

Using RTK-based GPS guidance for planting and inverting peanuts

GPS guidance of farm machinery has been adopted by increasingly larger segments of the farming community over the past decade because of the inherent gains in efficiency that it provides. The study was conducted for two consecutive years (2010 and 2011) on a working farm in Georgia, USA. The goal of our study was to quantify the yield benefit of using RTK-based automated steering (auto-steer) to plant and invert peanuts under a variety of terrain conditions. When all data are grouped together, auto-steer outperformed conventional by 579 kg/ha in 2010 and 451 kg/ha in 2011.

Delineation of Management Zones for Southern Root-Knot Nematode using Fuzzy Clustering of Terrain and Edaphic Field Characteristics

Management zones (MZs) for southern root-knot nematode (RKN) from the integration of terrain (TR) and edaphic (ED) field features might facilitate variable rate nematicide applications. This study was conducted on 11 coastal plain fields in the USA. The relationships between RKN populations and five soil ED and TR attributes (apparent soil electrical conductivity [shallow (ECa-s) and deep (ECa-d)], elevation (EL), slope (SL), and changes in bare soil reflectance) were analyzed using canonical correlation. Using two ED and TR data sets, canonical predictors were used for zone delineation. Although the results showed that the zones with RKN population above the RKN field average were associated with the lowest values of ECa-s, ECa-d, normalized difference vegetation index (NDVI), and SL with respect to field average values, zone segregation was enough using ECa-s and ECa-d data. The results suggest the potential for using soil properties to identify RKN risk zones.

Climate Change and ENSO Effects on Southeastern US Climate Patterns and Maize Yield

Climate change has a strong influence on weather patterns and significantly affects crop yields globally. El Niño Southern Oscillation (ENSO) has a strong influence on the U.S. climate and is related to agricultural production variability. ENSO effects are location-specific and in southeastern U.S. strongly connect with climate variability. When combined with climate change, the effects on growing season climate patterns and crop yields might be greater than expected. In our study, historical monthly precipitation and temperature data were coupled with non-irrigated maize yield data (33–43 years depending on the location) to show a potential yield suppression of ~15% for one °C increase in southeastern U.S. growing season maximum temperature. Yield suppression ranged between −25 and −2% among locations suppressing the southeastern U.S. average yield trend since 1981 by 17 kg ha−1year−1 (~25%), mainly due to year-to-year June temperature anomalies. Yields varied among ENSO phases from 1971–2013, with greater yields observed during El Niño phase. During La Niña years, maximum June temperatures were higher than Neutral and El Niño, whereas June precipitation was lower than El Niño years. Our data highlight the importance of developing location-specific adaptation strategies quantifying both, climate change and ENSO effects on month-specific growing season climate conditions.

Cotton Yield Response to Variable Rate Nematicides According to Risk Zones

Cotton (Gossypium hirsutum L.) lint yield losses by southern root-knot nematode [Meloidogyne incognita] (RKN) have increased during the last 20 years. Site-specific management (SSM) of nematicides is a promising method to reduce yield losses, increase profitability and reduce adverse environmental impacts associated with excess allocations of agrochemicals. The impact of two nematicides applied at two rates on RKN population density and lint yield were compared across previously determined RKN risk zones in commercial fields during the 2007 growing season. Root knot nematode risk zones were delineated in 2006 using fuzzy clustering of elevation and slope of the terrain, normalized difference vegetation index (NDVI) calculated from a bare soil spectral reflectance, and apparent soil electrical conductivity [shallow (ECa-shallow) and deep (ECa-deep)]. Four different treatments of nematicides were randomly allocated among blocks that spanned the entire length of the fields. Test bare soil spectral reflectance plots (16 rows by 100 feet long) including the four treatments were also randomly selected within each zone to collect RKN population density, soil water content, and plant height, root galling, and final yield. In general, there were no benefits associated with a high rate of Telone (6 gal ac-1) versus a lower rate of 3 gal ac-1. Similarly, the higher Temik rate of 6 lbs ac-1 did not provide additional nematicide control compared to the low rate (3 lbs ac-1). Comparing treatment results across management zones, Telone provided better RKN control compared to Temik in high risk zones, comprised of more coarse-textured, sandy soil. However, in low risk zones, which were comprised of relatively heavier textured soil compared to the high risk areas, the application of Temik would provide sufficient nematicide control. The results from this study clearly showed that RKN control and final yield varied with respect to the nematicide type and rate across management zones (MZ). These results are promising and support the idea of variable rate nematicide applications based on RKN risk zones.

Geospatial Solutions For Precision Management Of Cotton Root Knot Nematodes

A central issue in cotton ( Gossypium) production is the reduction of yield by root knot nematodes (Meloidogyne incognita). Typically, nematode density is normally estimated by taking soil samples. However this method is expensive, and in some cases the samples taken from the fields do not accurately represent the spatial variability in nematode distributions. The main goal of this paper is to delineate nematode management zones and reduce field scale variability in nematodes for improved site specific management. The density of cotton root knot nematodes (RKN) in five cotton fields was measured four times during the growing season in 2005. Soil electrical conductivity (ECa), elevation, and remotely sensed data were collected as indirect indicators of nematode spatial distributions. A canonical analysis was used to determine which variables explained the greatest amount of variability in root knot nematodes. These variables were entered into a fuzzy clustering algorithm and used to delineate management zones. NDVI and elevation showed significant advantages in reducing the RNK within-zone variability compared to the whole field. However, in more uniform fields, zone delineation was less successful. Future research will evaluate this methodology over more variable soils and elevations and determine the utility of this methodology for the development of a risk index as well as for site-specific soil sampling and variable rate nematicide treatments.