Feeding Systems with Legumes to Intensify Dairy Farms - cgiar

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The model predicts expected milk yield from three different sources: from metabolizable energy (ME), from protein (P), and from amino acids availability (AA). However, the discussion will be centered on the results obtained from the first two sources because there was no information on the AA composition of the forages obtained from Quilichao nor was information available from the tropical forages library. Table 2. Consumption of sugarcane and Cratylia and predicted intake of Brachiaria decumbens for the different trials at Quilichao, Colombia. Triala Sugarcane Cratylia Brachiaria Total DM decumbens (% LW) ISR + sugarcane 4.43 0 5.04 9.47 2.12 ISR + 75% sugarcane + 25% legume 2.64 1.16 5.81 9.61 2.15 ISR + 50% sugarcane + 50% legume 1.48 2.28 5.83 9.59 2.15 ISR + 25% sugarcane + 75% legume 0.45 4.16 5.03 9.64 2.16 LSR + sugarcane 2.00 0 7.48 9.48 2.30 LSR + sugarcane + legume 2.92 0.62 6.02 9.56 2.32 HSR + sugarcane 2.00 0 6.96 8.96 2.17 HSR + sugarcane + legume 2.92 0.70 5.60 9.22 2.24 a. ISR = intermediate stocking rate; LSR = low stocking rate; and HSR = high stocking rate. As can be observed, the CNCPS model predicted the observed milk yields in both the low and high stocking rate trials, but failed to predict the observed milk yields for the intermediate stocking rate trials. This underprediction came from both the energy and protein portions, but the bias was larger in predicting milk yields using the protein portion. The probable cause of the low prediction of milk yields for the intermediate stocking rate using the protein portion is the indigestible dry matter which causes low microbial growth, due to high levels of NDF from both B. decumbens (69%-70%) and C. argentea (63%-66%). Based on Dr. Alice Pell’s comments, the high lignin content found in Cratylia could be the reason why the CNCPS model underpredicted observed milk yield. Based on Pell’s experience, lignin content of Cratylia leaves and stems should have been around 7%-8% but not the 16%-17% as found in the Quilichao work. The secondary compounds may be confounded with lignin, masking the overall effect of diet. 80
Table 3. Actual and predicted milk yield using the CNCPS model for the different trials at Quilichao, Colombia. Trial Predicted milk Actual milk yield yield Energy Protein Intermediate stocking rate (3 AU/ha) + Sugarcane 6.0 4.9 1.4 + 75% sugarcane + 25% legume 6.5 6.5 7.4 + 50% sugarcane + 50% legume 6.4 4.5 5.1 + 25% sugarcane + 75% legume 6.6 1.9 3.1 Low stocking rate (2 AU/ha) + Sugarcane 7.9 7.3 7.9 + Sugarcane + legume 8.2 8.1 8.1 High stocking rate (4 AU/ha) + Sugarcane 6.1 6.4 6.3 + Sugarcane + legume 7.0 7.5 8.0 Based on the Quilichao results, several hypotheses can be drawn: • Cows were selecting B. decumbens with a higher CP content than actual lab results. • Digestion rates for carbohydrate and protein fractions used were wrong. • Information about the pool sizes is inadequate. In addition, Table 4 presents the level of urea nitrogen found in milk (MUN) as well as the level predicted by the model. The difference could be due to the soluble protein level in the diet, which was higher than the CNCPS prediction. Because of the slow digestion rates associated with the B3 protein fraction, the CNCPS predicts that the neutral detergent insoluble protein (NDIP) makes little contribution to the rumen N pool. However, the NDIP contribution of tropical grasses to rumen N balance may be higher than that predicted by the CNCPS model. Thus, hypotheses (2) and (3) are probably valid. Increasing the CP content in the diet does not increase the protein available for milk yields in the same proportion because both the B. decumbens and the Cratylia diets are energy-deficient. However, the model 81
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Feeding Systems with Legumes to Int
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Holmann, Federico and Lascano, Carl
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List of Abbreviations and Acronyms
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At the CIAT-Quilichao station (Depa
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In collaboration with Nicaragua’s
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(Continued) Component Activity Outs
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Activity 2.1. On-farm evaluation of
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FINAL REPORT OF THE TROPILECHE CONS
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RESEARCH RESULTS Component 1: Optim
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during the rainy season, samplings
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Ncidence (%) 70 60 50 40 30 20 10 0
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Table 3. Effect of three types of p
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References Cipagauta, M.; Velasquez
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Table 1. Effect of incorporating St
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Evaluation of Milk Production Syste
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Table 2. Quality of milk produced b
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Table 1. Effect of level and freque
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Effects of Feeding Fresh and Ensile
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Table 1. Daily intake of dry matter
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argentea (taking into account cutti
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Evaluation of Cratylia argentea as
Page 43 and 44: A 3 x 3 Latin square changeover des
Page 45 and 46: argentea is recommended over the us
Page 47 and 48: The silos were left to ferment for
Page 49 and 50: References Argel, P. J. and Lascano
Page 51 and 52: y the Grain and Seed Research Cente
Page 53 and 54: Table 2. Yields per hectare in diff
Page 55 and 56: Table 1. Treatments evaluated for m
Page 57 and 58: Table 3. Dry matter and crude prote
Page 59 and 60: Activity 1.3 Functional relationshi
Page 61 and 62: increase in milk yield when offered
Page 63 and 64: Milk Production in Dual-Purpose Cow
Page 65 and 66: associated pasture. Also, the perce
Page 67 and 68: that of cows grazing sole cropped p
Page 69 and 70: Effect of Age of Regrowth and Cutti
Page 71 and 72: Average DM yields/plant at 60 days
Page 73 and 74: Percentage 70 60 50 40 30 20 10 0 F
Page 75 and 76: Effect of Supplementing Fresh and E
Page 77 and 78: Table 1. Average milk production, t
Page 79 and 80: Lucas, H. L. 1983. Design and analy
Page 81 and 82: U = overall mean Tj = effect of the
Page 83 and 84: In the case of king grass, estimate
Page 85 and 86: Milk production during the season o
Page 87 and 88: Jorge Vela IIAP, Peru Use of Stylos
Page 89 and 90: Use of Stylosanthes guianensis with
Page 91 and 92: Activity 2.3. Integration of inform
Page 93: Application of the Model to Researc
Page 97 and 98: Usefulness of the CNCPS Model The T
Page 99 and 100: Annual Meeting February 1998, Costa
Page 101 and 102: Regional Consultation Meeting Octob
Page 103 and 104: Ex-Ante Analysis of New Forage Alte
Page 105 and 106: Resource prices and capital investm
Page 107 and 108: Table 3. Direct production costs, g
Page 109 and 110: Production costs decreased as produ
Page 111 and 112: Ex-ante analysis for Nicaragua. The
Page 113 and 114: Real interest (%) 60 50 40 30 20 10
Page 115 and 116: With this alternative, the milk pro
Page 117 and 118: Early Adoption of Arachis pintoi in
Page 119 and 120: Table 2. Change in area of improved
Page 121 and 122: Table 5. Profitability of Arachis p
Page 123 and 124: with a dry season between May and A
Page 125 and 126: From 21% to 73% of the farmers grew
Page 127 and 128: Research Recommendations Based on r
Page 129 and 130: Materials and Methods Data for this
Page 131 and 132: Prices of Inputs/Outputs and Use of
Page 133 and 134: Table 3. Amount of feed supplements
Page 135 and 136: adoption level requires the lowest
Page 137 and 138: Table 6. Use of a simulation model
Page 139 and 140: The production cost/kg milk was low
Page 141 and 142: level of adoption can be regarded a
Page 143 and 144: income was lower or equal to 6 mont
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Table 10. Annual seed requirements
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Nicaragua. In addition, this level
Page 149 and 150:
Analysis of the Milk Market of Smal
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Results and Discussion Collection o
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Table 2. Characteristics of milk an
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The third need identified in Hondur
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artisan cheese factories milk with
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Tropileche Newsletter Federico Holm
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Tropileche on Internet Anderson Med
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Production of Brochures for Extensi
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with heavy soils and frequent rains
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systems, its use by ruminants can b
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Brachiaria brizantha CIAT 26110: Cu
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growing this cultivar in Costa Rica
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Technical bulletins Argel, P. J. an
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International Seminars Argel, P. J.
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LIST OF PROJECT PROPOSALS DEVELOPED
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Feeding Systems with Legumes to Intensify Dairy Farms - cgiar

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