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Jason S Cavadini - One of the best experts on this subject based on the ideXlab platform.
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effects of growth stage and Growing degree day accumulations on triticale forages 2 in vitro disappearance of neutral detergent fiber
Journal of Dairy Science, 2018Co-Authors: W K Coblentz, M S Akins, K F Kalscheur, G E Brink, Jason S CavadiniAbstract:ABSTRACT The use of winter triticale (X Triticosecale Wittmack) in dairy-cropping systems has expanded greatly in recent years, partly because of its value as a forage crop but also to improve land stewardship by providing winter ground cover. Our objectives were to use 2-pool and 3-pool nonlinear models to characterize in vitro disappearance of neutral detergent fiber (NDF) and then describe the relationship between estimated parameters from those models with plant growth stage or Growing degree days (GDD) >5°C for winter triticale forages harvested during 2016 and 2017 in Marshfield, Wisconsin. Forages were harvested from replicated field plots each year at growth stages ranging from stem elongation to soft dough. All NDF analyses included use of sodium sulfite and heat-stable α-amylase with residual fiber corrected for contaminant ash (asNDFom). Nonlinear 3-pool models for in vitro disappearance of asNDFom that included fast (Bfast) and slow (Bslow) disappearance pools as well as an associated disappearance rate for each (Kdfast and Kdslow, respectively) were easily fitted provided that a single discrete lag time was applied to both Bfast and Bslow pools to reduce the number of parameters to be estimated. An unresolved issue related to fitting 3-pool decay models was the incomplete recovery of asNDFom from immature triticale forages at 0 h, which was partially resolved with 2 approaches that produced similar estimates of Kdfast and Kdslow. Most parameters obtained from 2- and 3-pool decay models for asNDFom could be related to growth stage or GDD using polynomial regression techniques, often with high coefficients of determination (R2). For 3-pool models of asNDFom disappearance, Bslow increased with plant maturity, but the associated Kdslow ranged narrowly from 0.011 to 0.015/h and could not be related to growth stage or GDD by quartic, cubic, quadratic, or linear regression models. Despite different cultivars coupled with substantial differences in precipitation across years, single endpoint estimates of in vitro disappearance of asNDFom after 24, 30, or 48 h of incubation were closely related (R2 ≥ 0.906) to growth stage and GDD by linear or quadratic regression models that were generally similar across production years. Typical recommendations for harvesting triticale at boot stage to facilitate the planting of a double crop are strongly supported by the extensive 30-h in vitro disappearance of asNDFom at that growth stage, which was 63.1 and 64.8% of asNDFom during 2016 and 2017, respectively.
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effects of growth stage and Growing degree day accumulations on triticale forages 1 dry matter yield nutritive value and in vitro dry matter disappearance
Journal of Dairy Science, 2018Co-Authors: W K Coblentz, M S Akins, K F Kalscheur, G E Brink, Jason S CavadiniAbstract:ABSTRACT The use of triticale (X Triticosecale Wittmack) in dairy-cropping systems has expanded greatly in recent years, partly to improve land stewardship by providing winter ground cover. Our objective was to establish relationships relating indices of nutritive value with growth stage or accumulated Growing degree days >5°C for triticale forages grown in central Wisconsin. Replicated 3.7-m × 9.1-m plots were established following removal of corn for silage (fall 2015) and soybeans (fall 2016) and then harvested at various growth stages the following spring. Plants were assigned a numerical growth stage based on a linear staging system suitable for use as an independent regression variable. Response variables [e.g., dry matter (DM) yield, indices of nutritive value, and parameters from in vitro DM disappearance kinetics] were regressed on growth stage and Growing degree days using linear, quadratic, cubic, or quartic models. For spring 2016, the mean DM yield at the boot stage (3,804 kg of DM/ha) was only 30% of that observed at the soft dough stage of growth (12,642 kg of DM/ha). Although yields were reduced during spring 2017, primarily due to spring flooding, the relationship between respective yields at these growth stages was similar (1,453 vs. 5,399 kg of DM/ha). Regressions of DM yield (kg/ha) on growth stage for 2016 were explained by a cubic model (Y = 0.0663x3 − 9.44x2 + 595x – 9,810) compared with a simple linear response for 2017 (Y = 103x – 3,024); in both cases, coefficients of determination were very high (R2 ≥ 0.934). Many nutritional and in vitro DM disappearance characteristics were affected by the juxtaposition and balance of 2 generally competing factors: (1) increased concentrations of structural plant fiber coupled with concurrent lignification as plants matured and (2) the accumulation of highly digestible carbohydrate during seed head development. A comparison of respective energy yields between the boot and soft dough stages of growth for 2016 (2,488 vs. 8,141 kg of total digestible nutrients/ha) and 2017 (1,033 vs. 3,520 kg of total digestible nutrients/ha) suggests that yields of energy are greater at soft dough stage and are mostly driven by DM yield. An informed harvest management decision for lactating cows may still favor a boot-stage harvest because of superior nutritional characteristics, a need to plant double-cropped corn expeditiously, or both. Harvest timing of triticale forages for other livestock classes would appear to be more flexible, but prioritizing a subsequent double crop may reduce the effects on DM yield to a secondary consideration.
Haixin Chen - One of the best experts on this subject based on the ideXlab platform.
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impact assessment of climate change and later maturing cultivars on winter wheat growth and soil water deficit on the loess plateau of china
Climatic Change, 2016Co-Authors: Dianyuan Ding, Hao Feng, Ying Zhao, Wenzhao Liu, Haixin ChenAbstract:The use of adapted crop cultivars is an effective approach to cope with climate change. The objective of this study was to evaluate the impacts of climate change and the use of later-maturing cultivars on winter wheat yields and soil water deficit in dryland farming on the Loess Plateau of China. The later-maturing cultivars of winter wheat (Triticum aestivum L.) were generated by increasing the Growing degree day requirement according to Root Zone Water Quality Model 2. The results showed that the wheat yields and water use efficiency were significantly improved by the later-maturing cultivars and fluctuated along with precipitation trends. The downward trend of wheat growth duration due to climate warming conditions was effectively counteracted and even reversed by introduction of the later-maturing cultivars. Increased precipitation during the longer growth period of the later-maturing cultivars partly compensated for the wheat evapotranspiration, so that the soil water deficit level was maintained or even reduced. The results of this study contribute to gaining improved understanding of the effective phenotypic characteristics of dryland winter wheat to cope with climate change using model-based assessments.
W K Coblentz - One of the best experts on this subject based on the ideXlab platform.
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effects of growth stage and Growing degree day accumulations on triticale forages 2 in vitro disappearance of neutral detergent fiber
Journal of Dairy Science, 2018Co-Authors: W K Coblentz, M S Akins, K F Kalscheur, G E Brink, Jason S CavadiniAbstract:ABSTRACT The use of winter triticale (X Triticosecale Wittmack) in dairy-cropping systems has expanded greatly in recent years, partly because of its value as a forage crop but also to improve land stewardship by providing winter ground cover. Our objectives were to use 2-pool and 3-pool nonlinear models to characterize in vitro disappearance of neutral detergent fiber (NDF) and then describe the relationship between estimated parameters from those models with plant growth stage or Growing degree days (GDD) >5°C for winter triticale forages harvested during 2016 and 2017 in Marshfield, Wisconsin. Forages were harvested from replicated field plots each year at growth stages ranging from stem elongation to soft dough. All NDF analyses included use of sodium sulfite and heat-stable α-amylase with residual fiber corrected for contaminant ash (asNDFom). Nonlinear 3-pool models for in vitro disappearance of asNDFom that included fast (Bfast) and slow (Bslow) disappearance pools as well as an associated disappearance rate for each (Kdfast and Kdslow, respectively) were easily fitted provided that a single discrete lag time was applied to both Bfast and Bslow pools to reduce the number of parameters to be estimated. An unresolved issue related to fitting 3-pool decay models was the incomplete recovery of asNDFom from immature triticale forages at 0 h, which was partially resolved with 2 approaches that produced similar estimates of Kdfast and Kdslow. Most parameters obtained from 2- and 3-pool decay models for asNDFom could be related to growth stage or GDD using polynomial regression techniques, often with high coefficients of determination (R2). For 3-pool models of asNDFom disappearance, Bslow increased with plant maturity, but the associated Kdslow ranged narrowly from 0.011 to 0.015/h and could not be related to growth stage or GDD by quartic, cubic, quadratic, or linear regression models. Despite different cultivars coupled with substantial differences in precipitation across years, single endpoint estimates of in vitro disappearance of asNDFom after 24, 30, or 48 h of incubation were closely related (R2 ≥ 0.906) to growth stage and GDD by linear or quadratic regression models that were generally similar across production years. Typical recommendations for harvesting triticale at boot stage to facilitate the planting of a double crop are strongly supported by the extensive 30-h in vitro disappearance of asNDFom at that growth stage, which was 63.1 and 64.8% of asNDFom during 2016 and 2017, respectively.
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effects of growth stage and Growing degree day accumulations on triticale forages 1 dry matter yield nutritive value and in vitro dry matter disappearance
Journal of Dairy Science, 2018Co-Authors: W K Coblentz, M S Akins, K F Kalscheur, G E Brink, Jason S CavadiniAbstract:ABSTRACT The use of triticale (X Triticosecale Wittmack) in dairy-cropping systems has expanded greatly in recent years, partly to improve land stewardship by providing winter ground cover. Our objective was to establish relationships relating indices of nutritive value with growth stage or accumulated Growing degree days >5°C for triticale forages grown in central Wisconsin. Replicated 3.7-m × 9.1-m plots were established following removal of corn for silage (fall 2015) and soybeans (fall 2016) and then harvested at various growth stages the following spring. Plants were assigned a numerical growth stage based on a linear staging system suitable for use as an independent regression variable. Response variables [e.g., dry matter (DM) yield, indices of nutritive value, and parameters from in vitro DM disappearance kinetics] were regressed on growth stage and Growing degree days using linear, quadratic, cubic, or quartic models. For spring 2016, the mean DM yield at the boot stage (3,804 kg of DM/ha) was only 30% of that observed at the soft dough stage of growth (12,642 kg of DM/ha). Although yields were reduced during spring 2017, primarily due to spring flooding, the relationship between respective yields at these growth stages was similar (1,453 vs. 5,399 kg of DM/ha). Regressions of DM yield (kg/ha) on growth stage for 2016 were explained by a cubic model (Y = 0.0663x3 − 9.44x2 + 595x – 9,810) compared with a simple linear response for 2017 (Y = 103x – 3,024); in both cases, coefficients of determination were very high (R2 ≥ 0.934). Many nutritional and in vitro DM disappearance characteristics were affected by the juxtaposition and balance of 2 generally competing factors: (1) increased concentrations of structural plant fiber coupled with concurrent lignification as plants matured and (2) the accumulation of highly digestible carbohydrate during seed head development. A comparison of respective energy yields between the boot and soft dough stages of growth for 2016 (2,488 vs. 8,141 kg of total digestible nutrients/ha) and 2017 (1,033 vs. 3,520 kg of total digestible nutrients/ha) suggests that yields of energy are greater at soft dough stage and are mostly driven by DM yield. An informed harvest management decision for lactating cows may still favor a boot-stage harvest because of superior nutritional characteristics, a need to plant double-cropped corn expeditiously, or both. Harvest timing of triticale forages for other livestock classes would appear to be more flexible, but prioritizing a subsequent double crop may reduce the effects on DM yield to a secondary consideration.
B Franch - One of the best experts on this subject based on the ideXlab platform.
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improving the timeliness of winter wheat production forecast in the united states of america ukraine and china using modis data and ncar Growing degree day information
Remote Sensing of Environment, 2015Co-Authors: B Franch, E Vermote, Inbal Beckerreshef, M Claverie, Jianxi Huang, J Zhang, C Justice, J A SobrinoAbstract:Abstract Wheat is the most important cereal crop traded on international markets and winter wheat constitutes approximately 80% of global wheat production. Thus, accurate and timely production forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) developed an empirical generalized model for forecasting winter wheat production. Their approach combined BRDF-corrected daily surface reflectance from Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop type masks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the Growing season, percent wheat within the CMG pixel (area within the CMG pixel occupied by wheat), and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. In this study, we include Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts while conserving the accuracy of the original model. We apply this modified model to three major wheat-producing countries: the Unites States (US), Ukraine and China from 2001 to 2012. We show that a reliable forecast can be made between one month to a month and a half prior to the peak NDVI (meaning two months to two and a half months prior to harvest), while conserving an accuracy of 10% in the production forecast.
J A Sobrino - One of the best experts on this subject based on the ideXlab platform.
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improving the timeliness of winter wheat production forecast in the united states of america ukraine and china using modis data and ncar Growing degree day information
Remote Sensing of Environment, 2015Co-Authors: B Franch, E Vermote, Inbal Beckerreshef, M Claverie, Jianxi Huang, J Zhang, C Justice, J A SobrinoAbstract:Abstract Wheat is the most important cereal crop traded on international markets and winter wheat constitutes approximately 80% of global wheat production. Thus, accurate and timely production forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) developed an empirical generalized model for forecasting winter wheat production. Their approach combined BRDF-corrected daily surface reflectance from Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop type masks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the Growing season, percent wheat within the CMG pixel (area within the CMG pixel occupied by wheat), and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. In this study, we include Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts while conserving the accuracy of the original model. We apply this modified model to three major wheat-producing countries: the Unites States (US), Ukraine and China from 2001 to 2012. We show that a reliable forecast can be made between one month to a month and a half prior to the peak NDVI (meaning two months to two and a half months prior to harvest), while conserving an accuracy of 10% in the production forecast.
