Ammonia Emission, Manure Nutrients and Egg Production of Laying Hens Fed Distiller Dried Grain Diets

A USDA Natural Resources Conservation Service, Conservation Innovation Grant project coordinated by the United Egg Producers (UEP) conducted concurrent demonstrations in Iowa and Pennsylvania (PA) at commercial laying hen facilities. The goal was to document manure nutrient and gas emission improvements through the use of dried distiller’s grain with solubles (DDGS) diets and/or other dietary modifications while maintaining or improving hen productivity. Results of the PA trial are presented here. Diets containing 10% corn DDGS with (D+P) or without (D) the probiotic Provalen™ were compared to a corn-soybean based control diet (CON). The isocaloric, amino acid balanced diets were fed to three groups of 39,800 Lohmann hens in one house. Hens were 20-65 wk of age with each diet provided to 2 of 6 rows of stacked cages with manure belts (six decks high). Feed intake, water consumption, hen body weight (BW), egg production (EP,) egg case weight, mortality, feed cost (FC), and egg income (EI) were provided weekly by the cooperating egg company. Replicated monthly data, including egg weight (EW), albumen height (AH), Haugh units (HU), yolk color (YC), shell strength (SS) and shell thickness (ST), were determined from eggs collected from six 4-cage sections of hens on each diet. Replicated monthly samples of hen manure (fresh and from storage) were analyzed for moisture and major nutrients. Ammonia (NH3) gas measurements utilized a non-steady state flux chamber method coupled with photoacoustic infrared gas analyzer. There was no clear trend in the magnitude of NH3 emissions relative to the diets within the hen house as measured on the manure belt. At 32 and 36 wks of age, NH3 emissions were significantly (P \u3c 0.10) higher in D while D+P and CON were lower and similar. At 48 and 52 wks, NH3 emissions from D were similar to D+P and significantly lower than CON. Emission rate from belt manure averaged 0.42 ±0.025 g bird-1 d-1 for all treatments and dates. There was no significant impact of diet on BW, EW, HU, SS, or ST (P =0.10 to 0.66), however, CON hens had lower EP, AH, and YC compared to D and D+P hens (P=0.05). Fresh manure total phosphorus (P2O5) was higher for CON samples (P \u3c 0.05) while other major agronomic nutrients and moisture were not significantly different among treatments. Stored CON manure samples had increased moisture and NH4-N compared to those of D and D+P treatments (P \u3c 0.10). Weekly EI minus FC averaged $6,146,$6,215, and $6,209 for the CON, D, and D+P diets, respectively

Rencana Kerja Fakultas Peternakan Tahun 2021

Indikator Kinerja Sasaran Strategis pada Renstra Bisnis Unand 2020-202

Vegetative buffers for fan emissions from poultry farms: 2. ammonia, dust and foliar nitrogen

This study evaluated the potential of trees planted around commercial poultry farms to trap ammonia (NH3) and dust or particulate matter (PM). Norway spruce, Spike hybrid poplar, hybrid willow, and Streamco purpleosier willow were planted on five commercial farms from 2003 to 2004. Plant foliage was sampled in front of the exhaust fans and at a control distance away from the fans on one turkey, two laying hen, and two broiler chicken farms between June and July 2006. Samples were analyzed for dry matter (DM), nitrogen (N), and PM content. In addition, NH3concentrations were measured downwind of the exhaust fans among the trees and at a control distance using NH3 passive dosi–tubes. Foliage samples were taken and analyzed separately based on plant species. The two layer farms had both spruce and poplar plantings whereas the two broiler farms had hybrid willow and Streamco willow plantings which allowed sampling and species comparisons with the effect of plant location (control vs. fan). The results showed that NH3 concentration h− 1 was reduced by distance from housing fans (P ≤ 0.0001), especially between 0 m (12.01 ppm), 11.4 m (2.59 ppm), 15 m (2.03 ppm), and 30 m (0.31 ppm). Foliar N of plants near the fans was greater than those sampled away from the fans for poplar (3.87 vs. 2.56%; P ≤ 0.0005) and hybrid willow (3.41 vs. 3.02%; P ≤ 0.05). The trends for foliar N in spruce (1.91 vs. 1.77%; P = 0.26) and Streamco willow (3.85 vs. 3.33; P = 0.07) were not significant. Pooling results of the four plant species indicated greater N concentration from foliage sampled near the fans than of that away from the fans (3.27 vs. 2.67%; P ≤ 0.0001). Foliar DM concentration was not affected by plant location, and when pooled the foliar DM of the four plant species near the fans was 51.3% in comparison with 48.5% at a control distance. There was a significant effect of plant location on foliar N and DM on the two layer farms with greater N and DM adjacent to fans than at a control distance (2.95 vs. 2.15% N and 45.4 vs. 38.2% DM, respectively). There were also significant plant species effects on foliar N and DM with poplar retaining greater N (3.22 vs. 1.88%) and DM (43.7 vs. 39.9%) than spruce. The interaction of location by species (P ≤ 0.005) indicated that poplar was more responsive in terms of foliar N, but less responsive for DM than spruce. The effect of location and species on foliar N and DM were not clear among the two willow species on the broiler farms. Plant location had no effect on plant foliar PM weight, but plant species significantly influenced the ability of the plant foliage to trap PM with spruce and hybrid willow showing greater potential than poplar and Streamco willow for PM2.5(0.0054, 0.0054, 0.0005, and 0.0016 mg cm− 2; P ≤ 0.05) and total PM (0.0309, 0.0102, 0.0038, and 0.0046 mg cm− 2, respectively; P ≤ 0.001). Spruce trapped more dust compared to the other three species (hybrid willow, poplar, and Streamco willow) for PM10 (0.0248 vs. 0.0036 mg cm− 2; P ≤ 0.0001) and PM\u3e 10 (0.0033 vs. 0.0003 mg cm− 2; P = 0.052). This study indicates that poplar, hybrid willow, and Streamco willow are appropriate species to absorb poultry house aerial NH3–N, whereas spruce and hybrid willow are effective traps for dust and its associated odors

Pendugaan Kualitas Fisik Biji Jagung untuk Bahan Pakan Menggunakan Jaringan Syaraf Tiruan Berdasarkan Data Citra Digital

The Research intended to study the method of prediction of physical quality of corn kernel of feed stuff using Artificial Neural Network (ANN) based on variables of image data. The image data was used as input of ANN, and the character of corn kernel was the output. The variable of image were index red index, green index, blue index, hue, saturation, intensity, entropy, energy, contras, and homogeneity. The characteristic of corn were intact kernel, broken kernel, damage kernel and moldy kernel. There are two phase of application of ANN; training and validation. The training intend to calibrate the relationship between the image variable and corn kernel characteristics. The validation intend to examine the accuration of prediction. The result of research indicated that the intact kernel less accurate (70%) be predicted by image data, whereas broken kernel, damage kernel and moldy kernel can be predicted accurately (100%). The average of accuracy was 92.5 %. It was conclude that it was need to be improved the quality of image before processing the data to be input to the ANN

Pendugaan Kandungan Air, Protein, Llsin dan Metionin Tepung Ikan dengan Jaringan Syaraf Tiruan Berdasarkan Absorbsi Near Infrared

The objective of this study was to apply artificial neural network (ANN) to enable accurate and fast prediction of moisture, protein. lysine and methionine contents of ftishmeal. The several wevelenqths of near intrared absorbance, range from 900 to 2,000 nm, were selected for training and validating ANN on each chemical component by stepwise multiple linear regression analysis. Tne ANN with three, five. seven and nine nodes at hidden layer were trained using 35 samples for moisture and protein, 33 samples for lysine and 30 samples for metionine Validating was conducted on 10 independent samples. The results of validating indicated that the best of protoin prediction was achieved by ANN with seven nodes at hidden layer for moisture. five nodes for protein and methionine, and three nodes for lysine. The standard error of prediction, coefficient of variation and ratio of standard deviation and standard error of prediction respoctivety were 0.61%, 4.81%, and 6.89 for moisture contents; 2.99%, 6.43% and 3.34 for protein contents: 0.14%, 11.32% and 3.04 for lysine contents: and 0.07%. 10.50% and 2.16 for methionine contents. With the same data entry. the ANN could predict with better performance than did by multiple linear regressio

PENGARUH RADIASI SURYA DAN CURAH HUJAN TERHADAP UMUR DAN HASIL KACANG TANAHEffect of Solar Radiation and Rainfall on Growth and Yield of Peanut

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