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1. Cotton Disease Loss Estimates from the United States — 2022

Author

Faske, Travis, primary, Sisson, Adam, additional, Strayer-Scherer, Amanda, additional, Hu, Jiahuai, additional, Hutmarcher, Robert, additional, Grabau, Zane, additional, Small, Ian, additional, Kemerait, Bob, additional, Onofre, Rodgrigo Borba, additional, Price, Trey, additional, Watson, Tristan, additional, Allen, Tom, additional, Wilson, Bradley, additional, Idowu, Omololu John, additional, Collins, Guy, additional, Edmisten, Keith, additional, Byrd, Seth, additional, Mueller, John, additional, Kelly, Heather, additional, Isakeit, Tom, additional, Wheeler, Terry, additional, and Langston, David, additional

Published
2023
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4. Reevaluation of the degree day base 60°F concept in US cotton (Gossypium hirsutum L.) production

Author

Raper, Tyson B., primary, Ward, Rebecca, additional, Rushing, Cheyenne, additional, Brown, Steve, additional, Sandlin, Tyler, additional, Norton, Randy, additional, Hutmacher, Bob, additional, Snider, John L., additional, Fromme, Dan, additional, Dodds, Darrin, additional, Jones, Michael, additional, Edmisten, Keith, additional, Collins, Guy, additional, Byrd, Seth, additional, Griffin, James, additional, Maeda, Murilo, additional, Frame, Hunter, additional, Pieralisi, Brian, additional, Meeks, Calvin, additional, Whitaker, Jared, additional, Nichols, Robert L., additional, and Morgan, Gaylon, additional

Published
2023
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5. Reevaluation of the degree day base 60°F concept in US cotton (Gossypium hirsutumL.) production

Author

Raper, Tyson B., Ward, Rebecca, Rushing, Cheyenne, Brown, Steve, Sandlin, Tyler, Norton, Randy, Hutmacher, Bob, Snider, John L., Fromme, Dan, Dodds, Darrin, Jones, Michael, Edmisten, Keith, Collins, Guy, Byrd, Seth, Griffin, James, Maeda, Murilo, Frame, Hunter, Pieralisi, Brian, Meeks, Calvin, Whitaker, Jared, Nichols, Robert L., and Morgan, Gaylon

Abstract

Cotton (Gossypium hirsutumL.) management decisions to abet early growth, fruit set, boll maturation, and harvest preparation are often facilitated by prediction of the date when critical developmental stages are reached. In the United States, growing degree days calculated with a base 60°F (DD60s) are commonly used to predict cotton development. Observations suggest development of modern cultivars differs from previously established guidelines. The objectives were to (1) reevaluate DD60s required for an early, mid‐maturing and late maturing cultivar to reach key growth stages across the US Cotton Belt; and (2) determine if predictions of growth stages are strengthened by optimizing base temperature or including an upper threshold by growth stage. During 2018 and 2019, 22 field trials were established in 10 states. Plant growth stages were monitored weekly and air temperature was computed from interpolated surface observations weighted by a physical, geographic model. Observed DD60s to reach growth stages varied slightly by cultivar and region (≤85 DD60s and ≤130 DD60s, respectively). Required DD60s to reach growth stages exceeded most published ranges. Optimization of base temperature and inclusion of an upper threshold by growth stage did not substantially decrease errors in predicting date of growth stage occurrence. The DD55 and DD55 with an upper threshold of 86°F calculations resulted in slightly lower errors in predicting date of growth stage occurrence than the DD60 calculation. Although guidelines should be updated, it is unlikely slight modification in base temperature or upper thresholds will drastically increase the predictive ability over the DD60 calculation. Growing degree days base 60°F (DD60s) to developmental stages are greater than previously published guidelines.Beginning at square, earlier maturing cultivars reach each developmental stage earlier than later cultivars.Optimization of base temperature did not substantially increase predictive power over the DD60 calculation.Inclusion of an upper threshold only slightly increased the predictive power over a standard DD60 calculation.

Published
2023
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8. Performance of Tank Mix Partners with Isoxaflutole Across the Cotton Belt

Author

Foster, Delaney C., primary, Dotray, Peter A., additional, Baughman, Todd A., additional, Byrd, Seth A., additional, Culpepper, Alfred S., additional, Dodds, Darrin M., additional, Noland, Reagan L., additional, Nolte, Scott, additional, Norsworthy, Jason K., additional, Steckel, Lawrence E., additional, and Thompson, Corey N., additional

Published
2021
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11. Water Volume and Deposition Effects on Harvest-Aid Efficacy

Author

Griffin, James A., primary, Byrd, Seth, additional, Morgan, Gaylon D., additional, Dabney, Alan, additional, Raper, Tyson B., additional, Dodds, Darrin M., additional, Norton, Randy, additional, Jones, Andrea, additional, Collins, Guy D., additional, Cutts, Trey, additional, Edmisten, Keith L., additional, and Butler, Shawn A., additional

Published
2020
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17. Cotton Stage of Growth Determines Sensitivity to 2,4-D

Author

Byrd, Seth A., primary, Collins, Guy D., additional, Culpepper, A. Stanley, additional, Dodds, Darrin M., additional, Edmisten, Keith L., additional, Wright, David L., additional, Morgan, Gaylon D., additional, Baumann, Paul A., additional, Dotray, Peter A., additional, Manuchehri, Misha R., additional, Jones, Andrea, additional, Grey, Timothy L., additional, Webster, Theodore M., additional, Davis, Jerry W., additional, Whitaker, Jared R., additional, Roberts, Phillip M., additional, Snider, John L., additional, and Porter, Wesley M., additional

Published
2016
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20. Tips To Establish A Good, Healthy Stand.

Author

Hutmacher, Bob, Norton, Randy, Morgan, Gaylon, Maeda, Murilo, Byrd, Seth, Robertson, Bill, Meeks, Calvin, Fromme, Dan, Dodds, Darrin, Raper, Tyson, Wright, David, Freeman, Mark, Collins, Guy, and Frame, Hunter

Published
2019

32. Tips To Promote A Successful Stand.

Author

Maeda, Murilo, Morgan, Gaylon, Byrd, Seth, Meeks, Calvin, Robertson, Bill, Fromme, Dan, Dodds, Darrin, Freeman, Mark, Whitaker, Jared, Frame, Hunter, Raper, Tyson, Edmisten, Keith, and Wright, David

Published
2019

33. Prepare Now For Planting Season.

Author

Norton, Randy, Robertson, Bill, Maeda, Murilo, Morgan, Gaylon, Fromme, Dan, Raper, Tyson, Byrd, Seth, Meeks, Calvin, Dodds, Darrin, Wright, David, and Collins, Guy

Published
2019

34. Using Precipitation Forecasts to Irrigate Cotton.

Author

Christ, Emily H., Webster, Peter J., Collins, Guy D., Toma, Violeta E., and Byrd, Seth A.

Subjects
METEOROLOGICAL precipitation, RAINFALL probabilities, WEATHER forecasting
Abstract

In this experiment, precipitation forecasts were used to schedule irrigation for cotton (Gossypium hirsutum L.). Four irrigation treatments and two cotton varieties were evaluated at Stripling Irrigation Research Park located near Camilla, GA in 2014. Two treatments were irrigated based on forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) and The Weather Channel®'s mobile app. Irrigation amounts for these two treatments were determined by the Cotton Irrigation Schedule Suggested for High Yields (check-book recommendations). The third treatment was irrigated by the check-book method and the fourth was rainfed. Irrigation applied for each treatment was 34.8 cm, 26.7cm, and 31.9 cm for the ECMWF EPS, weather.com, and checkbook, respectively. Rainfed cotton received 16.56 cm in precipitation. All irrigation methods resulted in significantly higher yields than the rainfed cotton. Results suggest using precipitation forecasts to schedule irrigation could provide a convenient alternative to the check-book method. [ABSTRACT FROM AUTHOR]

Published
2015
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35. 2018 Season Draws To A Close.

Author

Collins, Guy, Wright, David, Dodds, Darrin, Raper, Tyson, Meeks, Calvin, Byrd, Seth, Robertson, Bill, Morgan, Gaylon, and Norton, Randy

Published
2018

36. Pre-Season Decisions.

Author

Hutmacher, Bob, Norton, Randy, Byrd, Seth, Morgan, Gaylon, Robertson, Bill, Milam, Mike, Fromme, Dan, Raper, Tyson, Dodds, Darrin, and Wright, David

Published
2018

37. The Relationship Between Sap Flow and Commercial Soil Water Sensor Readings in Irrigated Potato (Solanum tuberosumL.) Production

Author

Byrd, Seth, Rowland, Diane, Bennett, Jerry, Zotarelli, Lincoln, Wright, David, Alva, Ashok, and Nordgaard, John

Abstract

Many irrigation scheduling methods utilized in commercial production settings rely on soil water sensors that are normally purchased as off-the-shelf technology or through contracted services that install and monitor readings throughout the season. These systems often assume a direct relationship between the parameters measured by these soil water sensors (voltage, unitless values, or calibrated soil moisture values) and the water use and deficit stress of the crop. Because of this assumed relationship, these sensors are purported to be useful for triggering irrigation applications by monitoring relative changes in sensor values that represent either a “dry” or “wet” condition in the field. However, there is often little confirmation that these sensors accurately reflect crop water uptake or what soil depths will best represent that relationship. In an attempt to quantify the association between the use of soil water sensors and crop water use in a commercial potato field, measurements of soil water using capacitance probes and plant water use using sap flow sensors were monitored. Measurements were taken in two water application treatments: a normal (full) and partial irrigation schedule because it was hypothesized that the relative strength of the relationship between sensor reading and crop water use may be highly dependent on field soil water status. Relative soil moisture readings and plant water use data were compiled and both linear and quadratic regressions were performed. The correlation between sap flow and soil sensor readings was significant; but the relationship was relatively weak with the strength dependent on the soil depth that was monitored, indicating that care must be taken when utilizing sensor readings for irrigation scheduling. Muchos métodos de programación de riego que se utilizan en instalaciones de producción comercial se respaldan en sensores de agua en el suelo que generalmente se compran como tecnología “fuera de la plataforma” o a través de servicios contratados que instalan y le dan seguimiento a las lecturas a lo largo del ciclo. Estos sistemas con frecuencia asumen una relación directa entre los parámetros medidos por estos sensores de agua en el suelo (voltaje, valores sin unidades, o valores calibrados de la humedad del suelo) y el uso del agua y el agobio por déficit del cultivo. Debido a esta asumida relación, estos sensores son formalmente útiles para disparar aplicaciones de riego mediante el monitoreo de los cambios relativos en los valores del sensor que representan ya sea una condición “seca” o “húmeda” en el campo. No obstante, a menudo hay poca confirmación de que estos sensores reflejan con precisión la absorción del agua por el cultivo o a que profundidades del suelo representarán mejor esa relación. En un intento para cuantificar la asociación entre el uso de los sensores del agua del suelo y el uso del agua por el cultivo en una siembra comercial de papa, se dio seguimiento a las mediciones del agua en el suelo utilizando sondas de capacitancia y el uso de agua por la planta con sensores de flujo de savia. Se tomaron las mediciones en dos tratamientos de aplicación de agua: una programación normal (completa) y otra parcial de riego, porque se tuvo la hipótesis de que la fuerza relativa de la relación entre la lectura del sensor y el uso de agua por el cultivo pudiera ser altamente dependiente de la situación del agua del suelo en el campo. Se recopilaron datos de las lecturas de la humedad relativa del suelo y del uso de agua por la planta y se les hicieron regresiones lineales y cuadráticas. La correlación entre el flujo de savia y las lecturas de los sensores del suelo fue significativa primero bajo condiciones de agua limitada en el suelo; pero la relación fue relativamente débil con la fuerza dependiente de la profundidad del suelo que se monitoreó, indicando que se debe tener cuidado cuando se utilicen lecturas de sensores para la programación del riego.

Published
2015
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38. A Roller Coaster Of A Season For Some.

Author

Wright, David, Dodds, Darrin, Raper, Tyson, Meeks, Calvin, Byrd, Seth, Robertson, Bill, Maeda, Murilo, Morgan, Gaylon, and Fromme, Dan

Published
2018

39. Cotton Stage of Growth Determines Sensitivity to 2,4-D

Author

Byrd, Seth A., Collins, Guy D., Culpepper, A. Stanley, Dodds, Darrin M., Edmisten, Keith L., Wright, David L., Morgan, Gaylon D., Baumann, Paul A., Dotray, Peter A., Manuchehri, Misha R., Jones, Andrea, Grey, Timothy L., Webster, Theodore M., Davis, Jerry W., Whitaker, Jared R., Roberts, Phillip M., Snider, John L., and Porter, Wesley M.

Published
2015
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40. Highlights of 2018 auxin certification training in Texas.

Author

Dotray, Peter, Nolte, Scott, McGinty, Josh, Morgan, Gaylon, and Byrd, Seth

Subjects
AUXIN, PESTICIDE use regulations, HERBICIDE application, DICAMBA
Abstract

The article presents important points from 2018 auxin certification training in Texas, which was mandated by the U.S. Environmental Protection Agency and the Texas Department of Agriculture for any farmers planning to apply three newly formulated dicamba herbicides. Topics discussed include the reclassification of the herbicides XtendiMax, Engenia and FeXapan as Restricted Use Pesticides; recordkeeping requirements for the use of those herbicides; and application instructions.

Published
2018

41. Cotton boll distribution, yield, and fiber quality in response to irrigation

Author

Parsons, Hardy Zane, Lewis, Katie, Byrd, Seth A., and Ritchie, Glen

Subjects
Cotton, Deficit irrigation
Abstract

Cotton is one of the main crops produced on the High Plains of Texas. Because of the limited heat and growing season available in the high plains, maturity is critical to both yield and fiber quality of the cotton crop. A cotton plant produces fruit at regular intervals on adjacent fruiting sites up the plant; therefore, the maturity characteristics of a cultivar are tied to the distribution of bolls on the plant. However, a given cultivar may not show the same growth habits under different environmental conditions, such as irrigation rate. This study compares the effects of multiple deficit irrigation levels on within plant boll distribution, yield, and fiber quality of five different varieties: FiberMax 1830GLT (FM1830), FiberMax 2011GT (FM1830), FiberMax 2334GLT (FM2334), Stoneville 4747GLB2 (ST4747), and Stoneville 4946GLB2 (ST4946). The cotton was grown at the Texas Tech Quaker Research Farm in Lubbock, TX in 2014 and 2015. At harvest, two rows of each plot were harvested with a cotton stripper and the plots were weighed. A subsample of each plot was ginned, and lint yield and fiber quality were determined. For both years of research, irrigation level had a statistically significant effect on lint yield, length, uniformity, and strength. Cultivar also had a significant impact on lint yield, length, uniformity, strength, and bolls per plant. Cultivar had a significant effect on micronaire for 2014 but not 2015. The interaction of cultivar and irrigation level was not significant for either year of the study. FM1830 had the longest length for 2014, and FM2334 had the longest length in 2015. FM1830 and FM2334 were most uniform for 2014 and 2015. FM 1830 had the greatest strength for 2014 and 2015. FM2334 had the highest micronaire for 2014.

Published
2018

42. Soil potassium effects on cotton (Gossypium Hirsutum) growth, yield and quality in the texas high plains

Author

Bumguardner, Amee Robin, Morgan, Gaylon D., Ritchie, Glen L., Lewis, Katie, and Byrd, Seth A.

Subjects
Soil, Potassium, Cotton
Abstract

When comparing soil potassium (K) levels common in west Texas to the current Mehlich III-K critical level for cotton (Gossypium hirsutum L.), applications of fertilizer K are not often recommended. However, when K is applied to soil, positive responses in yield of cotton have been reported. The objectives of this research were to: 1) Evaluate the effect of different application methods and rates of K fertilizer on cotton growth, lint yield, and fiber quality; 2) Evaluate the effect of K fertilization on lint yield and fiber quality components within specific node zones; and, 3) Determine the effect of K fertilizer rates within irrigation levels on lint yield and boll distribution. In Lamesa muriate of potash (KCl) was applied using two methods, knife injected (0-0-15) and broadcast (0-0-60), and at New Deal KCl was applied using the knife injected method. Rates of K application included 0, 45, 90, 135, and 180 kg ha-1 with high and low irrigation levels. At Lamesa in 2016 lint yield was greater when 90 kg K ha-1 was applied broadcast under high irrigation (2,153 kg ha-1 lint) compared to the 180 kg ha-1 treatment under high irrigation and all K treatments under low irrigation. There were no differences in 2017 for yield at Lamesa. It can be concluded from this experiment that K rates affected bulk lint yield when applied by broadcasting compared to the injection application method in 2016 at the Lamesa, TX, location. At New Deal lint yield differences were not identified for K application rate, while a difference in irrigation was observed, where the high irrigation level increased machine harvested yield on average by 940 kg ha-1 in 2016 and 425 kg ha-1 in 2017. Similar to previous research, irrigation had an effect on boll distribution. At New Deal total bolls in 2016 under the low irrigation resulted in an 175,000 boll ha-1 increase at 135 kg K ha-1 over 45 and 90 kg K ha-1. Differences did not exist in 2016 for box picking yield between K treatments in New Deal. When 180 kg K ha-1 was applied total bolls and box picking yield under low irrigation increased in the early and middle node zones and total nodes over all other K application rates at New Deal in 2017. Based on box picking results from 2017, 180 kg K ha-1 significantly increased yield under the low irrigation in New Deal. In 2016, 135 kg K ha-1 and in 2017, 180 kg K ha-1 tended to have more bolls in the early node zone under the low irrigation and the middle node zone across both irrigation levels. Box picking helped determine that the early and middle node zones were able to utilize the K within the plant due to the increase in bolls and yield compared to the late and vegetative node zones. Our results in the overall bulk lint yield suggest further research is needed to better understand the dynamics of K and cotton production and the effects on lint yield between irrigation levels and application methods.

Published
2018

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