• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.119-126
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• 2008

Growth and yield prediction methods, ranging from whole-stand models to individual-tree models, have been developed for forest types managed for wood production. The resultant models are used for a host of purposes including inventory updating, management planning, evaluation of silvicultural alternatives, and harvest scheduling. Because of the large investment in developing growth and yield models for improved genotypes and silvicultural practices for loblolly pine (Pinus taeda) in the Southern United States, this region serves to illustrate approaches for modelling intensively managed forests. Analytical methods and computing power generally do not restrict development of reliable growth and yield models. However, long-term empirical observations on stand development, which are time consuming and expensive to obtain, often limit modelling efforts. Given that growth and yield models are used to project present volumes and to evaluate alternative treatment effects, data of both the inventory type and the experimental type are needed. Data for developing stand simulators for loblolly pine plantations have been obtained from a combination of permanent plots in operational forest stands and silvicultural experiments; these data collection efforts are described and summarized. Modelling is essential for integrating and synthesizing diverse information, identifying knowledge gaps, and making informed decisions. The questions being posed today are more complex than in the past, thus further accentuating the need for comprehensive models for stand development.

• Proceedings of the Safety Management and Science Conference
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• 2010.11a
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• pp.551-557
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• 2010

The research proposes the hypergeometric, binomial and Poisson yield models for defective and defect. The paper also presents the hypothesis test, confidence interval and control charts for DPU(Defect Per Unit) and DPO(Defect Per Opportunity). Especially the study considers the analysis of compound Poisson yield models using various DOU density distributions.

• Asian-Australasian Journal of Animal Sciences
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• v.8 no.4
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• pp.365-368
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• 1995

Lactation curves of dairy cows were generated using three models, namely; incomplete gamma function (model 1), polynomial inverse function (model 2) and non-linear regression (model 3). Secondary milk yield data of 27 cows which had completed 6 lactations were used in this study. Milk yield records (once a week) throughout the lactation and from the first three months of lactation were fitted to the models. Estimation of total milk yield by model 3 using the data once a week throughout the lactation resulted in smaller % bias and standard error than those generated from model 1 and 2. But, model 2 was more accurate in predicting the 305-day milk yield equivalent closer to actual yields with smaller bias % and error using partial records up to 3 months. Also, model 2 was able to estimate the time to reach peak yield close to the actual data using partial records and model 2 could be used as a tool to advise farmers on appropriate feeding and management practices to be adopted.

• Korean Journal of Organic Agriculture
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• v.25 no.2
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• pp.279-293
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• 2017

This study suggests the yield forecast models for autumn chinese cabbage and radish using crop growth and development information. For this, we construct 24 alternative yield forecast models and compare the predictive power using root mean square percentage errors. The results shows that the predictive power of model including crop growth and development informations is better than model which does not include those informations. But the forecast errors of best forecast models exceeds 5%. Thus it is important to establish reliable data and improve forecast models.

• Journal of Crop Science and Biotechnology
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• v.10 no.1
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• pp.33-38
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• 2007

Nitrogen management at the panicle initiation stage(PI) should be fine-tuned for securing a concurrent high yield and high quality rice production. For calibration and testing of the recommendation models of N topdressing rates at PI for target grain yield and protein content of rice, three split-split-plot design experiments including five rice cultivars and various N rates were conducted at the experimental farm of Seoul National University, Korea from 2003 to 2005. Data from the first two years of experiments were used to calibrate models to predict grain yield and milled-rice protein content using shoot fresh weight(FW), chlorophyll meter value(SPAD), and the N topdressing rate(Npi) at PI by stepwise multiple regression. The calibrated models explained 85 and 87% of the variation in grain yield and protein content, respectively. The calibrated models were used to recommend Npi for the target protein content of 6.8%, with FW and SPAD measured for each plot in 2005. The recommended N rate treatment was characterized by an average protein content of 6.74%(similar to the target protein content), reduced the coefficient of variation in protein content to 2.5%(compared to 4.6% of the fixed rate treatment), and increased grain yield. In the recommended N rate treatments for the target protein content of 6.8%, grain yield was highly dependent on FW and SPAD at PI. In conclusion, the models for N topdressing rate recommendation at PI were successful under present experimental conditions. However, additional testing under more variable environmental conditions should be performed before universal application of such models.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.11
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• pp.1551-1555
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• 2006

This study was carried out to investigate the use of three different mathematical models (Wood, Quadratic and Cubic) for describing the lactation curve of Akkaraman ewes. Data were collected from 42 ewes that were three years of age and from the same flock raised in The State Farm of $G{\ddot{o}}zl{\ddot{u}}$ in Konya Province. All ewes lambed in March. They were hand milked twice daily and the first milk test was performed with in the first month after lambing (mean = 27.8 day, SD = 4.26) in an attempt to describe the peak yield. The differences between estimated total milk yields by the models were not significant. The models were adequate for describing total milk yield. The differences between peak yields were not significant. The Wood model estimated the time of peak yield earlier than the other models and observed values (p<0.01). Especially the Cubic model's peak time was very close to really peak time (34.30 vs. 35.33 days). $R^2$ values of the models ranged from 85.85% to 96.20%. The Cubic model gave the best $R^2$ value (p<0.01). Correlation coefficients between descriptive values of the models changed from 0.32 to 1.00. Total milk yields of the models were highly correlated (above 0.99) with the total milk yield calculated by the Fleischmann method (p<0.01). As a result the Cubic model showed the best fit to the data collected from Akkaraman ewes and allowed a suitable description of the shape of the lactation curve.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2006.06b
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• pp.305-309
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• 2006

This paper presents and illustrates the application of a growth and yield model that supports both forest and mill side volume and value estimates. Traditional forest stand growth and yield models represent the forest landowner view of yield and economics. Predicted yields are estimates of what one would expect from a procurement cruise, and current stumpage prices are applied to investigate optimum management strategies. Optimum management regimes and rotation ages obtained from the forest side view are unlikely to be economically optimal when viewed from the mill side. The actual distribution of recoverable manufactured product and its value are highly dependent on mill technologies and configurations. Overcoming this limitation of growth and yield computer models necessitates the ability to predict and price the expected manufactured distribution of lumber, lineal meters of veneer, and tonnes of air dried pulp fiber yield. With these embedded models, users of the yield simulator can evaluate the economics of possible/feasible management regimes from both the forest and mill business sides. The simulator is a forest side model that has been modified to produce estimates of manufactured product yields by embedding models for 1) pulpwood chip size class distribution and pulp yield for any kappa number (Schultz and Matney, 2002), 2) a lumber yield and pricing model based on the Best Opening Face model developed by the USDA Forest Service Forest Products Laboratory (Lewis, 1985a and Lewis, 1985b), and 3) a lineal meter veneer model derived from peeler block tests. While the model is strictly applicable to planted loblolly pine (Pinus taeda L.) on cutover site-prepared land in the United States (US) Gulf South, the model and computer program are adaptable to any region and forest type.

• Korean Journal of Organic Agriculture
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• v.25 no.3
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• pp.555-567
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• 2017

This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper's yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.335-344
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• 2024

This study explores advanced machine learning techniques for improving crop yield prediction in smart farming, utilizing multi-temporal spectral data from drone-based multispectral imagery. Conducted in garlic orchards in Andong, Gyeongbuk Province, South Korea, the research examines the effectiveness of various vegetation indices and cutting-edge models, including LSTM, CNN, Random Forest, and XGBoost. By integrating these models with the Analytic Hierarchy Process (AHP), the study systematically evaluates the factors that influence prediction accuracy. The integrated approach significantly outperforms single models, offering a more comprehensive and adaptable framework for yield prediction. This research contributes to precision agriculture by providing a robust, AI-driven methodology that enhances the sustainability and efficiency of farming practices.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.607-614
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• 2016

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of $polynomials{\times}3$ types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.