Modeling Environmental Conditions in Poultry Production: Computational Fluid Dynamics Approach.

In recent years, computational fluid dynamics (CFD) has become increasingly important and has proven to be an effective method for assessing environmental conditions in poultry houses. CFD offers simplicity, efficiency, and rapidity in assessing and optimizing poultry house environments, thereby fueling greater interest in its application. This article aims to facilitate researchers in their search for relevant CFD studies in poultry housing environmental conditions by providing an in-depth review of the latest advancements in this field. It has been found that CFD has been widely employed to study and analyze various aspects of poultry house ventilation and air quality under the following five main headings: inlet and fan configuration, ventilation system design, air temperature-humidity distribution, airflow distribution, and particle matter and gas emission. The most commonly used turbulence models in poultry buildings are the standard k-ε, renormalization group (RNG) k-ε, and realizable k-ε models. Additionally, this article presents key solutions with a summary and visualization of fundamental approaches employed in addressing path planning problems within the CFD process. Furthermore, potential challenges, such as data acquisition, validation, computational resource requirements, meshing, and the selection of a proper turbulence model, are discussed, and avenues for future research (the integration of machine learning, building information modeling, and feedback control systems with CFD) are explored.

Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation.

Traditional manual weighing systems for birds on poultry farms are time-consuming and may compromise animal welfare. Although automatic weighing systems have been introduced as an alternative, they face limitations in accurately estimating the weight of heavy birds. Therefore, exploring alternative methods that offer improved efficiency and precision is necessary. One promising solution lies in the application of AI, which has the potential to revolutionize various aspects of poultry production and management, making it an indispensable tool for the modern poultry industry. This study aimed to develop an AI approach based on the FL model as a viable solution for estimating poultry weight. By incorporating expert knowledge and considering key input variables such as indoor temperature, indoor humidity, and feed consumption, FL-based models were developed with different configurations using Mamdani inferences and evaluated across eight different rearing periods in Samsun, Türkiye. This study's results demonstrated the effectiveness of FL-based models in estimating poultry weight. The models achieved varying average absolute error values across different age groups of broilers, ranging from 0.02% to 5.81%. These findings suggest that FL-based methods hold promise for accurate and efficient poultry weight estimation. This study opens up avenues for further research in the field, encouraging the exploration of FL-based approaches for improved poultry weight estimation in poultry farming operations.

Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils.

Information on spatial distribution and potential sources of heavy metals in agricultural lands is very important for human health and food safety. In this study, pollution degree of lead (Pb), cadmium (Cd), and nickel (Ni) in Yüksekova Plain, located on the border in the southeastern part of Turkey, was evaluated by geoaccumulation index (Igeo), modified contamination factor (mCdeg), and Nemerow pollution index (PINemerow) combined with spatial autocorrelation using deep learning algorithms. A total of 304 soil samples were collected from two different depths (0-20 and 20-40 cm) in the study area, which covered 17.5 thousand ha land. Covariates were determined for spatial distribution models of Pb, Cd, and Ni by factor analysis (FA). Spatial distribution models for surface soils were developed using pedovariables (silt, sand, clay lime, organic matter, electrical conductivity, pH, Ca, and Na) determined by the FA and Igeo and mCdeg values by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. The estimation success of models for different depths was assessed by root mean square error (RMSE), mean absolute percent error (MAPE), and Taylor diagrams. The RMSE and MAPE values showed a strong correlation between heavy metal contents and the covariates. The RMSE values of ANN-Ni0-20, ANN-Ni20-40, ANN-Pb0-20, ANN-Cd0-20, and ANN-Cd20-40 models (0.01240, 0.07257, 0.0039, 0.00045, 0.00044, and 0.04607, respectively) confirmed the success of the models. Likewise, the MAPE values between 0.2 and 8.5% indicated that all models were very good predictors. In addition, the Taylor diagrams showed that the estimation performance of ANFIS and ANN models are compatible. The IgeoNi and IgeoPb values in both models at both depths indicated that strongly to extremely polluted (4-5) areas are quite high in the study area, while the IgeoCd values revealed that unpolluted areas are widespread. The mCdeg index value showed a moderate to high contamination at the first depth, while very high contamination at the second depth in most of the study area. Spatial distribution of PINemerow revealed that moderate pollution (2-3) is common in both soil depths of the study area. The PINemerow of subsurface layer was between 0.91 and 1 (warning limit class) in a small part of the study area. The results showed that vertical mobility of heavy metals is closely related to pedovariables. In addition, the ANN and ANFIS models are capable of exhibiting the heterogeneity in the spatial distribution pattern of high variation in the data. Thus, the locations with extreme contamination have been accurately determined. The pollution indices calculated considering the commonly used international reference values revealed that heavy metal pollution in some part of the study area reached the detrimental levels for human health and food safety. The results suggested that the pollution indices were more successful than simple heavy metal concentrations in interpreting the pollution risk levels. High-resolution spatial information reported in this study can help policy makers and authorities to reduce heavy metal emissions of pollutants or, if possible, to eliminate the pollution.

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon.

This study aimed to produce a soil organic carbon (SOC) content map with high accuracy and spatial resolution using the most effective factors in the model. The spatial SOC estimation success of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), Multi-Layered Perception Network (MLP) and MLP-OK Hybrid models were compared to obtain the most reliable model in estimating the SOC content. The study area was located in Besni district in the Southeastern Anatolia Region of Turkey. Total of 132 surface (0-30 cm) soil samples were collected from the covers 1330 km2 land and analyzed for SOC, lime, clay and sand content and soil reaction included in the estimation models. Mean annual precipitation and temperature, elevation, compound topographic index, enhanced vegetation and normalized difference vegetation index, were also used as the inputs in the modelling. The spatial distribution of SOC was determined using a MLP and a two-stage ensemble model (MLP-OK) combining the estimation of OK residuals. Soil surveys and covariates were used to train and validate the MLP-OK hybrid model. The MLP-OK model provided a more accurate estimation of SOC content with minimal estimation errors (ME: -0.028, 45 MAE: 0.042, RMSE: 0.066) for validation points compared to the other models. The MLP-OK model outperformed other models by 75.09 to 77.92%. The MLP-OK model estimated the lower and upper limits of the estimated and the measured values in a consistent manner compared to the other models. The spatial distribution map of SOC content obtained by ANN-kriging approach was significantly affected by ancillary variables, and revealed more detail than other interpolation methods in the northern, central, southwestern and southeastern parts of the study area. The results revealed that the assembling of MLP with OK model can contribute to obtain more reliable regional, national and global spatial soil information.

Computational Fluid Dynamics Modeling of a Broiler House Microclimate in Summer and Winter.

Appropriate microclimate conditions in broiler housing are critical for optimizing poultry production and ensuring the health and welfare of the birds. In this study, spatial variabilities of the microclimate in summer and winter seasons in a mechanically ventilated broiler house were modeled using the computational fluid dynamics (CFD) technique. Field measurements of temperature, relative humidity, and airspeeds were conducted in the house to compare the simulated results. The study identified two problems of high temperature in summer, which could result in bird heat stress and stagnant zones in winter, and simulated possible alternative solutions. In summer, if an evaporative cooling pad system was used, a decrease in temperature of approximately 3 °C could be achieved when the mean air temperature rose above 25 °C in the house. In winter, adding four 500-mm circulation fans of 20-m spacing inside the house could eliminate the accumulation of hot and humid air in the stagnant zones in the house. This study demonstrated that CFD is a valuable tool for adequate heating, ventilation, and air conditioning system design in poultry buildings.

Predicting field capacity, wilting point, and the other physical properties of soils using hyperspectral reflectance spectroscopy: two different statistical approaches.

In this study, we examined the ability of reflectance spectroscopy to predict some of the most important soil parameters for irrigation such as field capacity (FC), wilting point (WP), clay, sand, and silt content. FC and WP were determined for 305 soil samples. In addition to these soil analyses, clay, silt, and sand contents of 145 soil samples were detected. Raw spectral reflectance (raw) of these soil samples, between 350 and 2,500-nm wavelengths, was measured. In addition, first order derivatives of the reflectance (first) were calculated. Two different statistical approaches were used in detecting soil properties from hyperspectral data. Models were evaluated using the correlation of coefficient (r), coefficient of determination (R (2)), root mean square error (RMSE), and residual prediction deviation (RPD). In the first method, two appropriate wavelengths were selected for raw reflectance and first derivative separately for each soil property. Selection of wavelengths was carried out based on the highest positive and negative correlations between soil property and raw reflectance or first order derivatives. By means of detected wavelengths, new combinations for each soil property were calculated using rationing, differencing, normalized differencing, and multiple regression techniques. Of these techniques, multiple regression provided the best correlation (P < 0.01) for selected wavelengths and all soil properties. To estimate FC, WP, clay, sand, and silt, multiple regression equations based on first(2,310)-first(2,360), first(2,310)-first(2,360), first(2,240)-first(1,320), first(2,240)-first(1,330), and raw(2,260)-raw(360) were used. Partial least square regression (PLSR) was performed as the second method. Raw reflectance was a better predictor of WP and FC, whereas first order derivative was a better predictor of clay, sand, and silt content. According to RPD values, statistically excellent predictions were obtained for FC (2.18), and estimations for WP (2.0), clay (1.8), and silt (1.63) were acceptable. However, sand values were poorly predicted (RDP = 0.63). In conclusion, both of the methods examined here offer quick and inexpensive means of predicting soil properties using spectral reflectance data.

Spatial variability of depth and salinity of groundwater under irrigated ustifluvents in the Middle Black Sea Region of Turkey.

Information on the potential risk for soil salinity buildup can be very helpful for soil salinity management in irrigated areas. We evaluated the spatial and temporal variability of groundwater salinity (GWS) and groundwater depth (GWD), which are two of the most important indicators of soil salinity, by indicator kriging technique in a large irrigated area in northern Turkey. GWS and GWD were measured on a monthly basis from irrigation season (August 2003) to rainy season (April 2004) at 60 observation wells in the 8,187-ha irrigated area. Five indicator thresholds were used for GWS and GWD. The semivariogram for each of the thresholds for both variables was analyzed then used together with experimental data to interpolate and map the corresponding conditional cumulative distribution functions (CCDF). Risk for soil salinity buildup was greater in the irrigation season compared to that in the rainy season. The greatest risk for soil salinity buildup occurred in the eastern part of the study area, suffering from poor drainage problem due to malfunctioning drainage infrastructure, as indicated by the CCDF of GWS and GWD obtained in both seasons. It was concluded that a combination of mechanical and cultural measures should be taken in high-risk locations to avoid further salinity problems.

Assessment of spatial variability in some soil properties as related to soil salinity and alkalinity in Bafra plain in northern Turkey.

The objectives of this study were to assess the variability in soil properties affecting salinity and alkalinity, and to analyze spatial distribution patterns of salinity (EC) and alkalinity (ESP) in the plain, which was used irrigation agriculture with low quality waters. Soil samples were collected from 0-30 cm, 30-60 cm, 60-90 cm and 90-120 cm soil depths at 60 sampling sites. Soil pH had the minimum variability, and hydraulic conductivity (Ks) had the maximum variability at all depths. The mean values of pH, EC, ESP and Ks increased while the mean values of CEC decreased with soil depth. Values pH, EC and ESP were generally high in the east and northeastern sides. Soil properties indicated moderate to strong spatial dependence. ESP and pH were moderately spatially dependent for three of the four depths, EC exhibited moderate spatial dependence for one of the four depths, CEC had a moderate spatial dependence at all depths, and Ks exhibited a strong spatial dependence. EC, CEC, and ESP were considerably variable in small distances. The spatial variability in small distances of EC, CEC, pH and ESP generally increased with depth. All geostatistical range values were greater than 1230 m. It was inferred that the strong spatial dependency of soil properties would be resulted in extrinsic factors such as ground water level, drainage, irrigation systems and microtopography.

Spatial variability and monitoring of pb contamination of farming soils affected by industry.

In this study, the relationship between some physico-chemical properties of soils and lead contamination in soil due to emission from industrial operations in Samsun province of Turkey was investigated. The extent of timely contamination was studied by comparing the obtained results with the results of the study conducted in the same region in 1998. An area of 225 km(2) (15 km x 15 km), which was divided into 1000 x 1000 m grid squares (16 lines in the east and south directions), was selected within the industrial area. The total of 256 grid points was obtained and soil samples were collected from three depths (0-5, 5-15, and 15-30 cm) of each grid center in 2004. The total Pb concentrations of soil samples were determined as 65.84-527.04 microg g(-1) at 0-5 cm in depth, 58.50 - 399.54 microg g(-1) at 5-15 cm in depth, and 44.65-330.07 microg g(-1) at 15-30 cm in depth. DTPA-extractable Pb concentrations of soils were found to be in the range of 1.52-9.03 microg g(-1), 0.54-7.09 microg g(-1), 0.19-6.13 microg g(-1) at 0-5, 5-15, and 15-30 cm depths, respectively. There were significant relationships between both total or DTPA-extractable Pb concentrations and selected physico-chemical properties of soil. According to enrichment factor (EF) values calculated from the total Pb concentrations, 11.3% of the study area (225 km(2)) was enriched with Pb in high level, but 77% of the area was in significant enrichment level with Pb. The average total and DTPA-extractable Pb concentrations increased as 11 and 13%, respectively in comparison with the results of 1998.

Inverse effects of Polyacrylamide (PAM) usage in furrow irrigation on advance time and deep percolation.

The positive effects of Polyacrylamide (PAM), which is used as a soil conditioner in furrow irrigation, on sediment transport, erosion, and infiltration have been investigated intensively in recent years. However, the effects of PAM have not been considered enough in irrigation system planning and design. As a result of increased infiltration because of PAM, advance time may be inversely affected and deep percolation increases. However, advance time in furrow irrigation is a crucial parameter in order to get high application efficiency. In this study, inverse effects of PAM were discussed, and as an alternative solution, the applicability of surge flow was investigated. PAM application significantly increased the advance time at the rates of 41.3-56.3% in the first irrigation. The application of surge flow with PAM removed this negative effect on advance time, where there was no statistically significant difference according to normal continuous flow (without PAM). PAM applications significantly increased the deep percolation, 80.3-117.1%. Surge flow with PAM had significantly positive effect on the deep percolation compared to continuous flow with PAM but not compared to normal continuous flow. These results suggested that irrigation planning should me made based on the new soil and flow conditions because of PAM usage, and surge flow can be a solution to these problems.