The Nutrient Analysis of P. purpureum and S. splendida

50 1 2 3 4 5 6 7 8 60 80 b ab a Figure 28. The main effect levels of shade of P. purpureum on ash content . Subscripts with the same letter in the same column showed the significant different test by Least Square Determination LSD in p0.05 Figure 28 explained about the influence of levels of shade due to ash content. From figure above, it could be seen that an increment trend of ash percentage due to levels of shade. The highest ash content found in 80 of levels of shade. The average of ash content was 7.03 on P .purpureum and slowly decreased in the lower of levels of shade. However, as 11.54 of ash content was gradually depressed since it was cultivated underneath 80 levels of shade. The influence of shading, organic fertilizer and defoliation management on S. Splendida was observed. Table 20 provided clearly regarding ash content on S. Splendida. Table 20. The Measurement ash content of S. Splendida on Levels of Shade, organic fertilizer and defoliation management treatments Shading Level Organic Fertilizer Mgha Harvest interval times 40d 50d 60d 30 8.1 8.0 7.8 20 8.2 8.7 8.7 10 8.7 7.5 5.7 60 30 8.2 8.6 7.9 20 10.8 10.5 8.0 10 8.5 8.6 8.7 80 30 7.1 9.7 8.7 20 8.6 10.3 9.0 10 9.4 9.6 9.7 Table 20 showed the measurement of ash content on S. Splendida due to the varied experiment treatment. The range amount of ash content on S. Splendida was 5.71-10.87. As general, we obtain a higher percentage of ash 51 7 7.5 8 8.5 9 9.5 60 80 a ab b content in S. Splendida compared with P. purpureum. Ash content on P. purpureum was significantly different due to levels of shade treatment p0.05. In general, as the longer time of defoliation management, ash content was slowly decreasing. In S. splendida, the ash content was found in 50 days after plantation, and slowly decreasing after longer defoliation management. The additional organic fertilizer showed the lower amount of ash content in S. splendida. It was calculated that adding 10 Mgha of organic fertilizer found the average of lowest ash content. The influence of levels of shade on ash percentage could be seen obviously in Figure 29. Figure 29. The mean effect levels of shade of S. splendida on ash content . Subscripts with the same letter in the same column showed the significant different test by Least Square Determination LSD in p0.05 Statistical analysis by using Analysis of Variance ANOVA showed that levels of shade has a significantly different due to levels of shade p0.05 on ash percentage . The result indicated an influenced of levels of shade due to ash content. However, in the lower levels of shade 0 and 60, the less ash content produced. The trend was not only occurred in S. Splendida, but also in P. purpureum. In previous discussion, it stated that levels of shade have gradually changes to DM production. There was considerable evidence that the reducing radiation may changes the chemical composition of forage. Ash is a component reflected mineral content in plants. Mayland 1974 stated that the ash content of shaded forage is increased to levels sometimes twice that of un-shaded plants, because of higher concentration of K, Mg, Ca and P. The reduced intake of 52 shaded forage was attributed to its lower soluble sugar content. The information this study might probably useful for the following research on mineral content in forage. It could be used to determined diet of feed for the dairy cattle. The following nutrient analyze observed was crude fat. Crude fat fat was briefly known in the nutrient consideration mostly measured in animal feed. Fat is typically feed to increase the energy density of the diet. In an effort to support an energy-demanding function milk production, energy of dense nutrients fat is often included in the diet in small amount 3 to 5 of diet DM. Fat supplementation has other potential benefit, such as increased absorption of fat- soluble nutrients and reduced dustiness of feed. An additional positive response to fat supplementation has been improved fertility Staples et.al 1995. In this study, we would like to gain deeply information regarding fat content analysis. The analysis of fat analysis could be seen in Table 21. Table 21. The Measurement fat content of P. purpureum on Levels of Shade, organic fertilizer and defoliation management treatments Shading Level Organic Fertilizer Mgha Harvest interval times 40d 50d 60d 30 1.4 1.5 3.3 20 1.2 2.9 1.8 10 4.3 3.0 2.9 60 30 1.5 1.1 2.7 20 1.2 1.6 1.5 10 1.3 1.8 1.5 80 30 1.6 1.6 2.8 20 1.5 4.3 2.8 10 1.5 4.6 3.8 Table 21 showed the varied number of fat content on P. purpureum since it highly affected by shading, organic fertilizer and defoliation management. The fat percentage‘s was range from 1.24- 4.64, with the average 2.27. In general, as the longer time for harvesting, the respond of fat content was higher. 60 days after plantation, showing the highest average of fat content on P. purpureum. It has been known briefly that supplemental fat has dramatically increased milk yield in many studies; however, responses have been variable. Some of the variation may be due to depression of feed intake, when feeding supplemental fat given. Moreover, we also observed that levels of shade has a significantly effect due to fat content on P. purpureum P0.05 Figure 30. 53 0.0 0.5 1.0 1.5 2.0 2.5 3.0 60 80 a ab b Figure 30. The mean effect levels of shade of P. purpureum on fat content . Subscripts with the same letter in the same column showed the significant different test by Least Square Determination LSD in p0.05. From figure above, it could be seen the highest amount of fat content was 4.64, found in 80 Levels of shades. The influence of supplemental fat on milk fat percentage is varied and depends on fat composition and the amount of feed. Fat supplemental can positively influence reproductive performance of dairy cows. A summary of 20 studies indicated that the first service conception rate or overall conception rate was increase in 11 of studies Staples et. al 1995. In most situation, total dietary fat should not exceed 6-7 percent of dietary DM. Feeding higher concentrations of fat can result in reduced DM intake, even if fat has minimal effect on rumen fermentation Schauff and Clark 1992. The other information provided regarding fat content on S. splendida Table 22. Table 22. The Measurement fat content of S. splendida on Levels of Shade, organic fertilizer and defoliation management treatments Shading Level Organic Fertilizer Mgha Harvest interval times 40d 50d 60d 30 2.4 1.5 1.8 20 3.0 2.2 1.5 10 2.1 1.8 1.9 60 30 2.7 1.9 1.5 20 1.9 1.6 1.4 10 2.1 1.6 2.1 80 30 1.9 2.7 1.9 20 1.6 1.5 2.2 10 3.1 1.8 2.1 54 Table 22 showed the measurement of fat content on S. Splendida since it planted in different experiment treatment. The range of fat content on S. Splendida was varied from 1.53-3.17, with the average 2.07. We observed that S. splendida has lower fat content compared with P. purpureum. In this study, defoliation management has a significance effect on fat content p0.05. Figure 32 showed the fat content as the effect of defoliation management. Figure 31. The main effect levels of shade of S. splendida on fat content . Subscripts with the same letter in the same column showed the significant different test by Least Square Determination LSD in p0.05 On Figure 31, it could be seen the main effect of fat content on S. Splendida as the effect of level of shades. As generally, the study showed that there was a trend on increasing of crude fat content as the higher number of Levels of shade. The average of data gained showed that the increment for 16.35 f as the higher Levels of shade. The other component of feed that influenced to milk production was dietary protein. Dietary protein generally refers to crude protein CP, which is defined for feedstuff as the nitrogen N content x 6.25. The definition is based on the assumption that the average N content of feedstuff is 16g per 100 g of protein. Increasing the protein concentration of the diet of lactating dairy cows can often increase milk production. Daily milk production increased linearly from 36.6 to 38.6 kg as the dietary protein content increased from 138 to 23.9 DM basis; Grinset. al. 1991 . 0.0 0.5 1.0 1.5 2.0 2.5 40 50 60 days a b ab 55 Crude protein could reflect the limitation factor of feed that given to the dairy cattle. Furthermore, the quality of feed was determined by the availability of crude protein. In addition, feed price also highly related with the protein contain. In Indonesia, mostly feeder contain higher protein has the higher price, due to its effect to the milk production resulted. Now a days, many dairy farmers were trying to fill protein contain on its feed in order they would be able to get higher price. On this research, the amount of crude protein contain was showed in Table 23. Table 23. The Measurement protein content of P. purpureum on Levels of Shade, organic fertilizer and defoliation management treatments Shading Level Organic Fertilizer Mgha Harvest interval times 40d 50d 60d 30 7.6 8.6 6.4 20 8.0 7.2 6.9 10 6.3 7.4 6.0 60 30 8.0 9.0 8.2 20 7.8 9.1 7.0 10 8.6 8.6 8.1 80 30 9.3 11.3 10.1 20 13.8 10.7 12.0 10 14.5 13.2 9.8 Table 23 showed the measurement of protein content on P. purpureum. The amount of protein content was varied since it influenced by many factors such as shading, fertilizer and defoliation management. We observed that the protein on P. purpureum, was 6-14, with the average of 9.16. From table 23 it could be seen in 0 of levels of shade, in 40 days after plantation, the highest protein content was found if the additional of organic fertilizer was added for 20 Mgha. In the longer time defoliation management 50 days, the organic fertilizer was added for 30 Mgha reflected highest protein content. In longest time of defoliation management, the requirement of organic fertilizer was reducing in order to gain the maximum protein content. Unlike 0 of levels of shade, within the higher levels of shade, the additional organic fertilizer played important part. In 60 levels of shade, the highest of protein content was found in 10 Mgha for 40 days after plantation, and followed by 20 Mgha for 50 and 30 MgHa for 60 days after plantation. In 80 of levels of shade, it could be gained that to obtain highest protein content was required 10 Mgha of organic fertilizer in 40 and 50 56 2 4 6 8 10 12 14 60 80 b ab a days, while it was needed higher as 20 Mgha for 60 days after plantation. In general, the additional of organic fertilizer was highly required, especially underneath shading condition, and longer time of defoliation management. Levels of shade had a significance effect due to protein content p0.05 Figure 32. Figure 32. The main effect levels of shade of P. purpureum on protein content . Subscripts with the same letter in the same column showed the significant different test by Least Square Determination LSD in p0.05 Figure 32 showed clearly the influenced of levels of shade due to protein content on P. purpureum. The less number of irradiance accepted by plants, showed the increment of protein content . From figure above it could be obtained that the average of highest protein content was 11.73, since it was planted in 80 levels of shade. We also observed the similar trend that found in S. splendida, due to protein content as the impact of experiment treatments Table 24. Table 24. The Measurement protein content of S. splendida on Levels of Shade, organic fertilizer and defoliation management treatments Shading Level Organic Fertilizer Mgha Harvest interval times 40d 50d 60d 30 16.7 14.2 13.5 20 16.6 13.3 12.2 10 14.7 13.0 11.8 60 30 19.7 17.4 15.2 20 19.0 16.0 13. 1 10 16.5 12.6 12.5 80 30 22.4 18.6 18.8 20 22.3 18.1 17.2 10 19.8 17.6 17.1 57 5 10 15 20 25 60 80 b ab a Table 24 showed the measurement of protein content of S. splendida since it was planted in different experiment treatments. The data showed the range of protein content was 12-22. This amount was higher compared with P. purpureum. It highly related with the kind of species. The additional of organic fertilizer showed the high impact on S. splendida. From Table 34, it could be seen that the highest of protein content was found in the additional of 30 Mgha organic fertilizers, either in 0 of levels of shade or 60 and 80 levels of shade. Defoliation management leaded to the slowly number of protein content measurement. The data in Table 24 showed, in 60 days of after plantation, protein content was present the lowest content. As the analysis statistic driven by ANOVA, it was stated that there was a significance difference on crude protein content due to the levels of shades p0.05. Moreover, levels of shade have dramatically impact due to protein content Figure 33. Figure 33. The mean effect levels of shade of S. splendida on protein content . Subscripts with the same letter in the same column showed the significant different test by Least Square Determination LSD in p0.05 Figures 33 showed the main effect of levels of shade that dramatically effected protein content on S. splendida. Protein content was increasing rapidly as the less irradiance accepted by plants. It was calculated that the highest protein content was found in 80 levels of shade, as 19.13. The equivalent result could be seen in S. splendida that presented the highest crude protein percentage in the less irradiance accepted by plant. From this data, it could be inferred that crude protein percentage has the relationship with the availability of sunlight. The least of the average of protein content was gained in full irradiance accepted by the 58 plant. The average of crude protein content was increasing for 26.95 in P.purpureum and 23.41 in S. splendida. The data has been showed that the less of irradiance then the more nitrogen content produced. The amount of nitrogen content determines the crude protein percentage. The similar trend showed by Sirait et.al 2005, based on their research on tropical grass Paspalum notatum, Brachiaria humidicola, Stenotaphrum secundatum showed the significance different on protein content due to levels of shade. They obtained higher protein content in forage with shades compared with unshade forage. Though the biomass production was declining due to levels of shade, the enhancement of nutrients could be gained in forage. The information might be used as the consideration on dietary feed for the dairy cattle. The following information regarding nutrient content was crude fiber. Crude fiber analysis was the last nutrient compound observed on this study. Crude fiber- carbohydrate was the major source of energy in diet feed to dairy cattle and usually comprises 60-70 percent of the total diet. The main function of carbohydrates is to provide energy for rumen microbes and the host of animal. A secondary, but essential, function of certain types of carbohydrate is to maintain the health of the gastrointestinal extract. The information regarding crude fiber content of P. purpureum could be seen on Table 25. Table 25. The Measurement fiber content of P. purpureum on Levels of Shade, organic fertilizer and defoliation management treatments Shading Level Organic Fertilizer Mgha Harvest interval times 40d 50d 60d 30 29.8 29.1 29.9 20 28.6 31.2 29.4 10 29.5 30.2 29.1 60 30 31.4 31.2 30.5 20 32.3 32.1 29.8 10 31.1 30.4 30.3 80 30 28.6 30.9 28.6 20 28.1 31.1 29.4 10 26.8 30.3 29.2 Table 25 showed the measurement of crude fat on P. purpureum since it was planted on the different experiment treatments. It was gained that the range of crude fiber content was 26-32 of total dry matter content. Moreover, we also 59 28 29 30 31 40 50 60 days observed that crude fiber was highly influenced by levels of shade and defoliation management. It could be seen on Figure 34, the influence both the shading treatment and harvesting management. 1 2 Figure 34. The main effect 1 levels of shade and 2 defoliation management of P. purpureum on fiber content . Subscripts with the same letter in the same column showed the significant different test by Least Square Determination LSD in p0.05 Figure above described the influence of defoliation management due to crude fiber content . From the figure above it could be understand that as generally there was a trend on the increment of crude fiber content influenced by Levels of shade and Defoliation management. Crude fiber content was gradually increasing since it planted in the higher levels of shade. In this study the measurement of optimum sun availability was 60 to produce highest crude fiber content. Crude fiber percentage found gradually decreasing since it planted in 80 of levels of shade. It could be seen that the highest crude fiber percentage found in 50 days after cut in P.purpureum. It was the maximum time to produce crude fiber content. As 30.75 of crude fiber content were calculated as the highest ones. However levels of shade have an impact to crude fiber content. Crude fiber content on S. splendida was provided in Table 26. 28 29 29 30 30 31 31 32 60 80 b a b a b b 60 5 10 15 20 25 30 40 50 60 days b a a Table 26. The Measurement fiber content of S. splendida on Levels of Shade, organic fertilizer and defoliation management treatments Shading Level Organic Fertilizer Mg ha -1 Harvest interval times 40d 50d 60d 30 18.5 27.2 26.9 20 16.5 21.1 24.8 10 18.5 28.8 25.4 60 30 14.0 24.3 25.3 20 13.8 21.5 25.7 10 15.9 19.8 22.6 80 30 15.1 22.6 23.5 20 9.47 22.7 22.9 10 15.4 24.4 24.7 Table 26 showed the measurement of crude fiber content on S. splendida. From table above, it could be seen that the range of crude fiber was 9.47-28.85. This number was lower compared with P. purpureum. Furthermore, crude fiber content in S. splendida was affected directly by defoliation management Figure 35 Figure 35. The main effect defoliation management of S.Splendia on fiber content . Subscripts with the same letter in the same column showed the significant different test by Least Square Determination LSD in p0.05 Figure 35 described the main effect of crude fiber content due to defoliation management. Based on the statistical analysis, it was gained that the different harvest time has a direct impact due to crude fiber analysis p0.05. It could be seen that the highest crude fiber content was found in 60 days after plantation. In this study, it was the optimum time for S. splendida, when the crude fiber could be achieved. It was calculated that the highest crude fiber content was 24.70. It was the maximum time to produce crude fiber content. However levels 61 of shade have an impact to crude fiber content. The information might be useful to determine the dietary of crude fiber content, especially for the lactating dairy cattle. 62 200 300 400 500 600 700 800 900 1000 2007 2008 2009 2010 2011 x 1 to n population head milk production tonnes

CHAPTER IV 4.1 General Discussion

The dairy farming rolled as the main sector for providing milk production in Indonesia, therefore sustainability of dairy farming was desired to be concerned. Dairy farming was disparate with others sectors of livestock production. If it compares with others livestock production system; such as beef cattle, that could be supported by others feed-sorghum, corn, oats, Etc. In this case, feed competed with human‘s food, therefore it hardly significance on food storage. Dairy farming was highly connected with plant-forages, as its feed and the dairy itself animal, also for milk production. The advantage should be the suitable key for improving dairy farming practices in Indonesia. It is well known that the main feed of dairy cattle was forage, which is highly supported milk production. Nowadays, forage scarcity became unruly in Indonesia, regarding to less number of areas for foraging. Ideally, the area needed for keeping 1 cattle is approximately 2 Ha. The dairy cow requires 25 kg of fresh forage with 87 moisture content or equals to 4 kg DM at least. Then the population reaches 597.129 heads approximately, so the amount of forage needed per day would be 14.928,2 Mg, which adds up to 5.448.793 Mg per year. However, forage problem will be a serious challenge for the dairy farmers today. Overview the dairy farming condition for five years 2007-2011, Indonesia‘s dairy cattle population showed the increment by years. Figure 36. Figure 36. Overview the dairy cattle population and milk production in Indonesia. 63 It was considered that the average of dairy population was elevated rapidly for 10.71. Also, milk production was enlarging firmly with an average 11.21 respectively, Indonesia Statistic of Livestock 2011. Additionally, the majority of dairy farming system was smallholder, where located mainly in Java Island, on the highland area, more than 700 meters above sea level, which the environment was better suited for dairy farming development. The fastest growth of dairy farming was found in East Java with the average of annual growth of 16.17. It was followed by West Java with 7.17 annual growth and Central Java with 5.86 annual growth Table 27. Table 27. Distribution of Dairy cattle in Indonesia Java, Sumatra and others island during 2007-2011. Province 2007 2008 2009 2010 2011 East Java 139.277 212.322 221.743 231.408 296.262 Central Java 116.260 118.424 120.677 122.489 149.931 West Java 103.489 111.250 117.352 120.475 139.992 Yogyakarta 5.811 5.652 5.495 3.466 3.522 Jakarta 3.685 3.355 2.920 3.238 2.728 Other areas: Sumatra Island 2.093 2.290 4.343 4.768 2.383 Other areas: Others Island Sulawesi, Kalimantan, Bali, NTB, NTT, Papua 3.452 4.284 2.171 2.571 2.311 Total 374,067 457.577 474.701 488.448 597.129 Source: Statistical Livestock 2011. From the Table 27, it could be seen that West Java is the second fasten growth of dairy farming. West Java is the most populated area in Java Island, with more than 48 of totally Java‘s civilization Statistical Bureau 2011. The heavily number of land utilization for civilization, encourage the limitation of agriculture area. Moreover, this situation generated to the less amount of plantation areas for foraging. Consequently, dairy farmers were meeting the requirement of forage by various ways, including planting forage in the forest. Since agroforestry was taking part in forage supply, then the evaluation of the forage supply in agroforestry system was conducted. The data obtained from the real Agroforestry system and from field experiment research were combining in enhancing a model of sustainability dairy farming. This model, were focusing the calculation of carrying capacity in Agroforestry system. The study was 64 analyzing the carrying capacity of agroforestry resources based on total digestive nutrient TDN supply and demand situation in Lembang, West Java. TDN was used as an approach to estimate the energy value of feedstuffs in order to calculate its total digestible nutrient TDN level using summative equation based upon analyzable components of feedstuffs. In 2009 there were 1502, 78 Ha forest that has potential as the forage area plantation. This amount used to be forest production area where has beneficial effect for forge plantation BPN 2009. Based on the initial information, then the calculation on TDN potency could be conducted. It was calculated as the TDNA Total Digestible Nutrient Available. TDNA refers to the availability of TDN provided in forest, in supplying TDN amount for the feed of dairy cattle in Lembang. The calculation of TDNA Mgyr was carrying in two varied ways. The first was the actual TDNA in agroforestry system TDNA without management improvement. It was emphasize on the actual data including the actual levels of shade, biomass production, dry matter production and percentage of TDN. However, the calculation might show the actual potency that occurred in the real agroforestry system. Then, we were also trying to make some future scenario in order to enhance the different of TDNA, in actual and simulation scenario TDNA with management improvement. In contrast, TDNA with management was gained by the initial information in the laboratory field then adjusted in the real agroforestry system. In adjusting data we also required several information such as, the levels of shade, biomass production, and the management improvement additional organic fertilizer and defoliation management. The calculation of TDNA was expected describing pronounced information about the effect on management improvement on TDNA. It also, could be projected as the comparisons on TDNA without and with management improvement. In previous discussion it was clearly informed about the nutrient contents in the real Agroforestry system was highly effected by levels of shade. In fact that different sites by its density showed the different amount of nutrient contents. Moreover, this information leaded to the amount of TDNA calculation in 65 Agroforestry system. Based on the nutrient analyses we could gain the TDN amount in Agroforestry system. In addition we also acquired the information regarding TDND Total Digestible Nutrient Demand. It was refers to as the calculation on the demand of TDN required by the dairy cattle. Nevertheless, the demand of TDN on the cattle was different by its stage of age. Since the dairy cattle farming structure were divided into several groups based on the growing stage of the cattle; dairy cow, heifers, calf and bull. In fact, on the different growing stage had different requirement of TDND. Moreover, we also attained the information about the amount of dairy cattle were occurring in Lembang, West Java. That information, regarding the amount of dairy cattle and the requirement of TDND was provided in Table 28. Table 28. The information of dairy cattle population and TDND based on its growing stage. No Dairy cattle structure Amount LSU TDN demand kgday 1 Dairy cow 11409 10.65 2 Heifers 2664 4.84 3 Calf 1164 2.88 4 Bull 635 5.82 Total 15872 Note : The calculation of TDN based on LSU Livestock Standard Units. The amount of TDN obtained from Japanese Livestock. Table 28 described the different amount of TDN demand based on LSU Livestock Standard Unit. LSU facilitated the aggregation of livestock from various species and age as per convention, via the use of specific coefficients established initially on the basis of the nutritional or feed requirement of each type of animal. The amount of TDND was pronounced seen in dairy cow. However, it could be understood, as it related for not only for basal metabolism but also for milking production. Furthermore, the calculation of TDN demand was counted for a year. Therefore we could gain the information about how large agroforestry system could support the dairy cattle in Lembang, West Java. From the basic understanding it could be gained that the annual demand of dairy farming totally 15872 LSU was required TDN approximately 35.895,15 Mgyr. This amount could be supported by feeder including forage, crop residue, 66 2000 4000 6000 8000 10000 12000 14000 Current Situation Adopt Mechanism Forage management T DN X1 M g Yr concentrate Ect. In this study, we would like obtaining the information about the ability of Agroforestry in supporting forage production. Therefore, it was gained that TDN as 7.012,04 Mgyr in the real Agroforestry system in Lembang. In another hand, it affirmed that TDNA without management improvement supported 19.53 of totally TDND of the dairy cattle. Moreover, we also verified the amount of TDNA with management improvement. Since the data from field experiment research was adjusted as the future scenario with the management improvement. Likewise, the additional organic fertilizer and defoliation management has been conducted in order to improve forage production and its quality. We were calculating as 12.239,56 Mgyr approximately might produce as TDNA with management improvement. It could be gained that 34.09 of TDNA was calculating for supporting forage production in Agroforestry system with management improvement. Figure 38 showed the comparisons of TDNA with and without management also TDND for the dairy cattle in Lembang, West Java. Figure 37. The comparisons both TDND and TDNA without and with management improvement in Agroforestry system in Lembang. Figure 37 described the chart of TDN Demand and Supply from Agroforestry system in Lembang, West Java. From figure above it could be seen that the amount of TDN Demand with improvement rapidly increase TDN for 42.71. This information might be useful for the dairy farmers in Lembang in enhancing dairy farming sustainability. 67 The benefits created by agroforestry practices are both economic and environmental. Agroforestry may increase farm profitability in several ways : 1 the total output per unit area of treecroplivestock combinations is greater than any single component alone, 2 crops and livestock protected from damaging effects of wind are more productive, and 3 new products add to the financial diversity and flexibility of farming enterprise Molua 2005. Since the objective of sustainability was to ensure the balancing aspect of ecology and economy, then the economy aspect needed to be concerned. In this part, the calculation of economic conducted as the feed cost that paid by farmers. The economic calculation was conducted by calculating the forage given kg and additional feed that required by dairy cattle to reach the nutrient balancing. However, this model would be useful for farmer‘s consideration in determining the feed cost. In the last chapter, we further discuss about the economics calculation due to the forage forest plantation in Agroforestry system. The forage production was calculation as the amount of forage that produced MghaYr. The amount of forage production then calculated as the feed input in economics analysis. The feed cost was also calculated based on the nutrients requirement. In this chapter we stressed on the dairy cow, which needed a huge of feed for basal metabolic and milk production. We assumed, as 10.65 kgday TDND was required, then we could gain the number of the total daily need. Table 29 described clearly the requirement of the dairy cow with the weight 450 kg. Table 29. The nutritional requirement of the dairy cow with the weight 450 Kg Table 29 described that the nutritional requirement of dairy cattle was divided into basal metabolism for basic life and for maintenance milk production. However, the limitation of feed was stressed on crude protein and TDN. It was calculated the daily need of nutrient requirement was 2.026 gramday and TDN as 10.65 Kgday. The nurient requirement of the dairy cow weight 450 kg CP grday TDN Kgday Basal Metabolism 916 5.70 Target Milk Production 1110 4.95 Total daily need 2026 10.65 68 5,000 10,000 15,000 20,000 25,000 30,000 Low Density Middlle Density High Density D ai ly Feed C ost IDR Feed for the dairy cow was provided not only by forage, but also an additional feed such kinds of concentrate and crop residue. In this study, farmers in Lembang were using Tofu waste and bakery waste. The information regarding nutrient content on feeding composition was providing in Table 30. Table 30. Nutrient content from feeding composition of the dairy cow Feeding Composition Nutrient Content Price IDR DM CP TDN Feeding Kg Forage 17.51 11.33 58.78 46.26 6.939,41 Concentrate 86.00 15.00 65 6.28 9.418,60 Crop Residue Tofu Waste 10.00 22.00 60 13.61 3402,72 Crop Residue Bakery Waste 91.60 10.90 82.70 1.23 430.39 Total 19.760.74 Note : DM requirement calculated as 3 of body weight NRC. From table 30 it could be seen that the nutrient content from feeding composition of the dairy cow. It was calculated as the daily feed cost that have to be paid by the farmers as 19.760,74 IDR days. It was the feed with DM 17.51, CP 11.33 and TDN 58.78 from the experiment site. However, shade has dramatically impact ether on the quantity or its quality. We stressed, whether the data from field experiment research conducted in the real agroforestry system by adjusting levels of shade treatment on the agroforestry system. In figure 38 it could be seen that the comparisons of the daily feed cost paid by farmers in the different plot observation. Figure 38. The comparisons of daily feed cost paid by farmers in low, middle and high density respectively.