Texas, USA 2010 - International Herbage Seed Group
Texas, USA 2010 - International Herbage Seed Group Texas, USA 2010 - International Herbage Seed Group
for the largest plants all occurring in the Willamette Valley, except for a single one in the easternSnake River Valley of southern Idaho. The next best locations (stars) occur over a broader set ofregions, including the Palouse Hills and the Columbia Basin in eastern Washington.One obvious concern with the methods we used to identify optimal plant locations is that theyare based on a single estimate of production. Because the specific crops grown within individualfields often change from year to year, a logical question is what impact this yearly variation hason the efficiency of plant siting. In other words, if plant locations are optimized for crop (andstraw) distribution patterns of one year (e.g., 2005), how well would those locations function ascentralized collection points for another year (e.g., 2006)? Since the bioenergy conversion plantsare unlikely to be mobile, a relatively simple way to evaluate the impact of yearly variation incropping patterns was to measure how much straw was available around plants whose locationsand collection distances were optimized for one year when a second year‟s straw distribution wasassumed. Practical limitations in programming methods used to optimize plant locations causedsome variability to exist in amount of straw present within the defined ranges around each planteven when the same year was used to define locations (and collection ranges) and measure strawavailability. Using the CV of the straw availability at each plant for the “same year” analysis asthe standard, a ratio of the CVs can be calculated showing how much less stable the straw supplywould be in some other year compared to the one used to locate the plants. The worstcombination we found was when medium-sized plants were located based on 2007 strawdistribution and tested using 2005 straw distribution, with a CV ratio of 11.6-fold (Table 2). Thesmallest CV ratios occurred when the 3-year average straw distribution was used to define plantlocations, with ratios for 2005, 2006, and 2007 ranging from 1.7 to 2.2 X for the smallest plants,2.6 to 3.9 X for the medium sized plants, and 1.5 to 2.2 X for the largest plants. The individualCVs generally followed a pattern of slowly decreasing with increasing plant size, with mean CVsfor all combinations of years-defining and year-testing straw availability averaging 50.0, 32.3,and 27.1% for the smallest-, medium-, and largest-sized plants.In a “young” straw as bioenergy industry, yearly variation in cropping practices and straw yieldsaround individual plants will merely generate small changes in the distance required to supplysufficient straw for plants. In a “mature” bioenergy industry, the yearly variations will likely alsoimpact how close to full capacity the plants can operate and the prices paid for straw. The largestscale straw-to-bioenergy plants currently under development in the Willamette Valley aredesigned to utilize 150 million kg y -1 . Even a plant that large would only need 2.3% of the totalavailable straw in the PNW. As a consequence, there is ample opportunity for market forces todetermine how much straw will continue to be exported as livestock feed, how much will beconverted into electricity and other energy products, and what mix of small-scale, on-farm andlarge-scale, industrial park bioenergy projects will operate to convert the straw into bioenergy.45
Table 1. Average distances required to provide sufficient straw to supply bioenergy conversion plants foreach 10 percentile increment in total straw assigned using 3-year average density.Incremental Percentiles of Total Available Straw Assigned to Optimal Plant Site LocationsState 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%10 6 kg y -1 capacity Average Range Required for Adequate Straw to Meet Plant Capacity (km)Idaho 1.2 1.5 1.7 1.8 2.0 2.1 2.3 2.9 4.9 31.2Oregon 0.9 1.2 1.4 1.5 1.7 1.9 2.1 2.4 3.4 19.2Washington 1.5 1.7 1.8 2.0 2.1 2.4 2.8 4.0 5.9 16.6entire PNW 1.2 1.4 1.6 1.8 1.9 2.1 2.4 3.0 4.6 23.010 7 kg y -1 capacityIdaho 3.7 4.3 4.8 5.5 6.1 6.9 8.2 11.5 17.6 88.9Oregon 2.3 2.7 3.1 3.5 4.0 4.9 5.8 7.4 11.1 39.6Washington 4.6 5.1 5.5 6.0 6.9 8.2 9.9 12.3 18.0 66.5entire PNW 3.5 4.0 4.4 4.9 5.5 6.6 7.7 10.2 15.4 64.910 8 kg y -1 capacityIdaho 14.0 16.4 17.5 18.1 25.3 27.5 29.0 46.3 98.2 276.5Oregon 7.9 9.2 10.7 11.7 13.2 14.8 21.8 28.4 47.0 186.4Washington 14.8 16.2 19.6 20.9 24.7 26.4 32.2 39.5 44.7 296.2entire PNW 11.7 14.3 15.2 16.2 21.1 23.6 26.0 39.2 68.3 243.546
- Page 5 and 6: Seed yield components and yield per
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- Page 9 and 10: 16:15 - 16:30 Reliability of salini
- Page 11 and 12: Hotel expense is covered for night
- Page 13 and 14: 40,000 were slaves (McDonald, 2007)
- Page 15 and 16: Fig. 1. Texas AgriLife Research and
- Page 17 and 18: $7 billion for cattle, $3 billion f
- Page 19 and 20: principle and encourages both AgriL
- Page 21 and 22: eceived by growers, the above perce
- Page 23 and 24: seed conditioning plants are locate
- Page 25 and 26: Table 4.Hectares of open-field burn
- Page 27 and 28: system, a seed crop is produced fro
- Page 29 and 30: Fig. 1. Land resource areas of Texa
- Page 31 and 32: y land owners. Seed yields are low
- Page 33 and 34: The influence of planting density o
- Page 35 and 36: Simple correlation and regression a
- Page 37 and 38: Variation in seed shattering in a g
- Page 39 and 40: Seed retention (SR) was calculated
- Page 41 and 42: mm160120Precipitation8040020Km h -1
- Page 43 and 44: Young, B. A. (1986). A Source of Re
- Page 45 and 46: Several methods are commonly used f
- Page 47 and 48: Table 3. Effect of the length of ha
- Page 49 and 50: Alfalfa seed production in semi-hum
- Page 51 and 52: Rather near the meteorological stat
- Page 53 and 54: ReferencesBolaños-Aguilar E.D., Hu
- Page 55: ased bioenergy conversion plants wa
- Page 59 and 60: Figure 1. Optimized locations for 1
- Page 61 and 62: Perennial ryegrass (Lolium perenne
- Page 63 and 64: Relative Seed Yieldsingle composite
- Page 65 and 66: Flowers, M.D.; Hart, J.M.; Young II
- Page 67 and 68: Thus, similar to tissue tests, remo
- Page 69 and 70: Conclusion:Perhaps our most importa
- Page 71 and 72: Modelling critical NDVI curves in p
- Page 73 and 74: The five spectral reflectance measu
- Page 75 and 76: Harvest loss in ryegrass seed crops
- Page 77 and 78: Larger than expected harvest losses
- Page 79 and 80: Rolston, P.; Trethewey, J.; McCloy,
- Page 81 and 82: Optical sensors have the potential
- Page 83 and 84: Figure 2. Seed yield response to ap
- Page 85 and 86: Flowers, M. D., Hart, J.M., Young I
- Page 87 and 88: In 2010, France has launched the fo
- Page 89 and 90: Yield (% maximum)ConclusionThe resu
- Page 91 and 92: Plant N uptakeN unavailableSoil nit
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- Page 95 and 96: correspond to electrical conductivi
- Page 97 and 98: applied later in the fall was more
- Page 99 and 100: Figure 2. Establishment of five ove
- Page 101 and 102: The seed vigour testing was perform
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for the largest plants all occurring in the Willamette Valley, except for a single one in the easternSnake River Valley of southern Idaho. The next best locations (stars) occur over a broader set ofregions, including the Palouse Hills and the Columbia Basin in eastern Washington.One obvious concern with the methods we used to identify optimal plant locations is that theyare based on a single estimate of production. Because the specific crops grown within individualfields often change from year to year, a logical question is what impact this yearly variation hason the efficiency of plant siting. In other words, if plant locations are optimized for crop (andstraw) distribution patterns of one year (e.g., 2005), how well would those locations function ascentralized collection points for another year (e.g., 2006)? Since the bioenergy conversion plantsare unlikely to be mobile, a relatively simple way to evaluate the impact of yearly variation incropping patterns was to measure how much straw was available around plants whose locationsand collection distances were optimized for one year when a second year‟s straw distribution wasassumed. Practical limitations in programming methods used to optimize plant locations causedsome variability to exist in amount of straw present within the defined ranges around each planteven when the same year was used to define locations (and collection ranges) and measure strawavailability. Using the CV of the straw availability at each plant for the “same year” analysis asthe standard, a ratio of the CVs can be calculated showing how much less stable the straw supplywould be in some other year compared to the one used to locate the plants. The worstcombination we found was when medium-sized plants were located based on 2007 strawdistribution and tested using 2005 straw distribution, with a CV ratio of 11.6-fold (Table 2). Thesmallest CV ratios occurred when the 3-year average straw distribution was used to define plantlocations, with ratios for 2005, 2006, and 2007 ranging from 1.7 to 2.2 X for the smallest plants,2.6 to 3.9 X for the medium sized plants, and 1.5 to 2.2 X for the largest plants. The individualCVs generally followed a pattern of slowly decreasing with increasing plant size, with mean CVsfor all combinations of years-defining and year-testing straw availability averaging 50.0, 32.3,and 27.1% for the smallest-, medium-, and largest-sized plants.In a “young” straw as bioenergy industry, yearly variation in cropping practices and straw yieldsaround individual plants will merely generate small changes in the distance required to supplysufficient straw for plants. In a “mature” bioenergy industry, the yearly variations will likely alsoimpact how close to full capacity the plants can operate and the prices paid for straw. The largestscale straw-to-bioenergy plants currently under development in the Willamette Valley aredesigned to utilize 150 million kg y -1 . Even a plant that large would only need 2.3% of the totalavailable straw in the PNW. As a consequence, there is ample opportunity for market forces todetermine how much straw will continue to be exported as livestock feed, how much will beconverted into electricity and other energy products, and what mix of small-scale, on-farm andlarge-scale, industrial park bioenergy projects will operate to convert the straw into bioenergy.45