Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Modelling of Shre Drag Tilt Velocimeter (DTV) with Curvilinear, Gompertz and Artificial Neural Network Method

Different method of modelling presented in this paper on Shre Drag Tilt Velocimeter non-linear data. The idea of different non-linear modelling method is to know which makes more possible to describe more accurate on interacting effects between velocities and tilt angle when compared among modellers. The models, which were used are static analytic approximation model, curvilinear bivariate regression model, Gompertz the classical growth model and Artificial Neural Network (ANN) model. Accuracy of the models was determined by mean square error (MSE), mean absolute deviation (MAD), bias and R Square. The datasets gathered from an experiment of Shre DTV at flume were divided into training data and testing data for the purpose of developing and validating all type of models. The difference between the model and the observed value become the forecasting error measurements. For the training data, the lowest MSE, RMSE and better R Square were noted for the Gompertz model. But ANN generalized better on testing data by obtaining lowest MSE, RMSE and higher R Square among others. ANN generalization result is 88.60%, Gompertz is 54.89%, curvilinear is 69.28% and static analytic is -1.29%. Lower bias was also for the neural network test data. As demonstrated by the bias values, only curvilinear model presenting overestimation model while other models produce little or no overestimation of the observed tilt response. Interpretations of the parameters estimation on Gompertz model have been attempted previously. However, focusing on the ability of Shre DTV to predict responses may be more practical than the relevance of parameter estimates

Neural Predictive Control of Broiler Chicken Growth

Active control of the growth of broiler chickens has potential benefits for farmers in terms of improved production efficiency, as well as for animal welfare in terms of improved leg health. In this work, a differential recurrent neural network (DRNN) was identified from experimental data to represent broiler chicken growth using a recently developed nonlinear system identification algorithm. The DRNN model was then used as the internal model for nonlinear model predicative control (NMPC) to achieve a group of desired growth curves. The experimental results demonstrated that the DRNN model captured the underlying dynamics of the broiler growth process reasonably well. The DRNN based NMPC was able to specify feed intakes in real time so that the broiler weights accurately followed the desired growth curves ranging from $-12$ to +12% of the standard curve. The overall mean relative error between the desired and achieved broiler weight was 1.8% for the period from day 12 to day 51

Using Model Predictive Control to Modulate the Humidity in a Broiler House and Effect on Energy Consumption

In moderate climate, broiler chicken houses are important heating energy consumers and hence heating fuel consumption accounts for a large part in operating costs. They can be reduced by constructional measures, which in turn lead to important costs as well. On the other hand, a software solution to reduce energy would lead to considerably less follow-up costs. The main objective of our work was to assess if it is possible to save energy with a software solution and eventually quantify the savings for a given broiler house in the Swiss Plateau. The investigation was carried out in simulation: the particular broiler house was measured, and a dynamical model for it was derived and validated. To actually search for a particular behaviour of the software that would lead to energy savings, model predictive control was used. The idea was not to specify a particular behaviour of the software but rather to let the software itself find the best behaviour in an exhaustive search. The simulations showed that energy savings can be realised mainly by letting the indoor humidity deviate from what usually is used as setpoint and hence take profit of the outdoor climate, which changes naturally during a 24-hour course. We used expert opinions to determine how long and large these setpoint deviations may be without harming the broilers. The simulations showed alsothat the light control and the biological activity of the animals reduced the potential savings

Method and approach Mapping for Agri-food Supply Chain Risk Management: A literature review

One of the agri-food characteristics is perishable product which made it has a higher chance damage risk from the farmer to the consumer. While issues around food security and associated risks are extremely important. Some methods or approaches have been used to identify and assess risks that occur in agri-food supply chain. The purpose of this paper was to identify the development of methods or approaches used to identify and assess the risks that occured in the agri-food supply chain. The articles search was undertaken through articles search on selected relevant journals of supply chain risk management for agri-food published from 2004 until 2014. A total of 77 randomly selected journal articles had been analyzed. These mapping were arranged in systematic stages, started from searches related supply chain risk management for agrifood. Furthermore, the articles identified and classified the methods or approaches for each stage of supply chain risk management, and were divided into three main stages: risk identification, risk assessment and risk mitigation. The last, the articles of risk identification are categorized into three groups : qualitative, semi-quantitative and qualitative.The mapping results showed that risk assessment supply chain for agri-food was much related to microbiology risk assessment. It related to the characteristics of agri-food products. Standard models used for risk assessment in supply chain for agri-food were based on integration of statistical analysis and simulation. The other techniques used included intelligent technique, optimization models and multi-criteria decision analysis. The literature on supply chain risk management for agri-food is so vast, complex and difficult to understand that a mapping of method and approach is needed and much value for the research community. Keywords :supply chain risk, risk identification, risk assessment, risk mitigation, agri-foo

Predicting chick body mass by artificial intelligence‑based models

The objective of this work was to develop, validate, and compare 190 artificial intelligence‑based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate‑controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21‑day‑old chicks) – with the variables dry‑bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks – was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro‑fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision‑making, and they can be embedded in the heating control systems.O objetivo deste trabalho foi desenvolver, validar e comparar 190 modelos baseados em inteligência artificial, para predizer a massa corporal de pintinhos de 2 a 21 dias de vida, submetidos a diferentes períodos e intensidades de estresse térmico. O experimento foi realizado com 210 pintinhos, em quatro túneis de vento climatizados. Um banco de dados com 840 conjuntos de dados (de aves de 2 a 21 dias) – com as variáveis temperatura de bulbo seco do ar, duração do estresse térmico (dias), idade das aves (dias) e a massa corporal diária dos pintinhos – foi utilizado para treinamento de rede, validação e testes dos modelos baseados em redes neurais artificiais (RNA) e redes “neuro-fuzzy” (RNF). As RNA mostraram-se mais precisas para se predizer a massa corporal de pintinhos de 2 a 21 dias de idade, submetidos às variáveis de entrada, e apresentaram R² de 0,9993 e erro‑padrão de 4,62 g. As RNA propiciam a simulação de diversos cenários, que podem auxiliar na tomada de decisões em relação ao manejo, e podem ser incorporadas nos sistemas de controle de aquecimento