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

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach.

California's almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented. These regulations require that growers apply nitrogen to meet, but not exceed, the annual N demand for crop and tree growth and nut production. To accurately predict seasonal nitrogen demand, therefore, growers need to estimate block-level almond yield early in the growing season so that timely N management decisions can be made. However, methods to predict almond yield are not currently available. To fill this gap, we have developed statistical models using the Stochastic Gradient Boosting, a machine learning approach, for early season yield projection and mid-season yield update over individual orchard blocks. We collected yield records of 185 orchards, dating back to 2005, from the major almond growers in the Central Valley of California. A large set of variables were extracted as predictors, including weather and orchard characteristics from remote sensing imagery. Our results showed that the predicted orchard-level yield agreed well with the independent yield records. For both the early season (March) and mid-season (June) predictions, a coefficient of determination (R 2) of 0.71, and a ratio of performance to interquartile distance (RPIQ) of 2.6 were found on average. We also identified several key determinants of yield based on the modeling results. Almond yield increased dramatically with the orchard age until about 7 years old in general, and the higher long-term mean maximum temperature during April-June enhanced the yield in the southern orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Remote sensing metrics such as annual maximum vegetation indices were also dominant variables for predicting the yield potential. While these results are promising, further refinement is needed; the availability of larger data sets and incorporation of additional variables and methodologies will be required for the model to be used as a fertilization decision support tool for growers. Our study has demonstrated the potential of automatic almond yield prediction to assist growers to manage N adaptively, comply with mandated requirements, and ensure industry sustainability

Effect of seed-beds on the cultivation of Radicchio (Cichorium intybus L., Rubifolium group)

The use of raised beds (i.e., ridged soil layers) to cultivate vegetables can improve soil quality, crop performance, water use and even reduce soil erosion. Although those advantages are well known and documented in the literature for many vegetable species, Radicchio, a particular variety of chicory, is usually cultivated in flat seed-beds and, therefore, the achievable benefits of raised beds for this vegetable are not yet known. The possibility to grow plants of Radicchio on raised beds is here evaluated by comparing the most important yield performances indicators deriving from the adoption of this practice (grown plants percentage, average head weight, marketable yields, and net incomes) with the same indicators recorded with a conventional practice making use of flat seed-beds. The results evidenced that raised beds for Radicchio had a statistically significant effect on the average head weight (+55.45 g plant-1 with 15-cm beds) with significant increases of field yields (up to +30.6%) and net incomes (up to +29.7%) on the trials with flat seed-beds. The experiment showed that the production of chicory can take an economical advantage from the use of raised seed-beds and, therefore, this system is preferable to the traditional cultivation practice

Modeling and Control Techniques in Smart Systems

Energy and food crisis are two major problems that our human society has to face in the 21st century. With the world’s population reaching 7.62 billion as of May 2018, both electric power and agricultural industries turn to technological innovations for solutions to keep up the increasing demand. In the past and currently, utility companies rely on rule of thumb to estimate power consumption. However, inaccurate predictions often result in over production, and much energy is wasted. On the other hand, traditional periodic and threshold based irrigation practices have also been proven outdated. This problem is further compounded by recent years’ frequent droughts across the globe. New technologies are needed to manage irrigations more efficiently. Fortunately, with the unprecedented development of Artificial Intelligence (AI), wireless communication, and ubiquitous computing technologies, high degree of information integration and automation are steadily becoming reality. More smart metering devices are installed today than ever before, enabling fast and massive data collection. Patterns and trends can be more accurately predicted using machine learning techniques. Based on the results, utility companies can schedule production more efficiently, not only enhancing their profitabilities, but also making our world’s energy supply more sustainable. In addition, predictions can serve as references to detect anomalous activities like power theft and cyber attacks. On the other hand, with wireless communication, real-time soil moisture sensor readings and weather forecasts can be collected for precision irrigation. Smaller but more powerful controllers provide perfect platforms for complicated control algorithms. We designed and built a fully automated irrigation system at Bushland, Texas. It is designed to operate without any human intervention. Workers can program, move, and monitor multiple irrigation systems remotely. The algorithm that runs on the controls deserves more attention. AI and other state of art controlling techniques are implemented, making it much more powerful than any existing systems. By integrating professional crop yield simulation models like DSSAT, computers can run tens of thousand simulations on all kinds of weather and soil conditions, and more importantly, learn from the experience. In reality, such process would take thousands of years to obtain. Yet, the computers can find an optimum solution in minutes. The experience is then summarized as a policy and stored inside the controller as a lookup table. Furthermore, after each crop season, users can calibrate and update current policy with real harvest data. Crop yield models like DSSAT and AquaCrop play very important roles in agricultural research. They represent our best knowledge in plant biology and can be very accurate when well calibrated. However, the calibration process itself is often time consuming, thus limiting the scale and speed of using these models. We made efforts to combine different models to produce a single accurate prediction using machine learning techniques. The process does not require manual calibration, but only soil, historical weather, and harvest data. 20 models were built, and their results were evaluated and compared. With high accuracy, machine learning techniques have shown a promising direction to best utilize professional models, and demonstrated great potential for use in future agricultural research

On-line Vis-Nir sensor determination of soil variations of sodium, potassium and magnesium

Among proximal measurement methods, visible and near infrared (Vis-Nir) spectroscopy probably has the greatest potential for determining the physico-chemical properties of different natural resources, including soils. This study was conducted to determine the sodium, potassium and magnesium variations in a 10. Ha field located in Karacabey district (Bursa Province, Turkey) using an on-line Vis-Nir sensor. A total of 92 soil samples were collected from the field. The performance and accuracy of the Na, K and Mg calibration models was evaluated in cross-validation and independent validation. Three categories of maps were developed: 1) reference laboratory analyses maps based on 92 points 2) Full-data point maps based on all 6486 on-line points Vis-Nir predicted in 2013 and 3) full- data point maps based on all 2496 on-line points Vis-Nir predicted in 2015. Results showed that the prediction performance in the validation set was successful, with average R2 values of 0.82 for Na, 0.70 for K, and 0.79 for Mg, average root mean square error of prediction (RMSEP) values of 0.02% (Na), 0.20% (K), and 1.32% (Mg) and average residual prediction deviation (RPD) values of 2.13 (Na), 0.97 (K), and 2.20 (Mg). On-line field measurement was also proven to be successful with validation results showing average R2 values of 0.78 (Na), 0.64 (K), and 0.60 (Mg), average RMSEP values of 0.04% (Na), 0.13% (K), and 2.19% (Mg) and average RPD values of 1.57 (Na) 1.68 (K) and 1.56 (Mg). Based on 3297 points, maps of Na, K and Mg were produced after N, P, K and organic fertilizer applications, and these maps were then compared to the corresponding maps from the previous year. The comparison showed a variation in soil properties that was attributed to the variable rate of fertilization implemented in the preceding year

Predicting spatiotemporal yield variability to aid arable precision agriculture in New Zealand : a case study of maize-grain crop production in the Waikato region : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agriculture and Horticulture at Massey University, Palmerston North, New Zealand

Precision agriculture attempts to manage within-field spatial variability by applying suitable inputs at the appropriate time, place, and amount. To achieve this, delineation of field-specific management zones (MZs), representing significantly different yield potentials are required. To date, the effectiveness of utilising MZs in New Zealand has potentially been limited due to a lack of emphasis on the interactions between spatiotemporal factors such as soil texture, crop yield, and rainfall. To fill this research gap, this thesis aims to improve the process of delineating MZs by modelling spatiotemporal interactions between spatial crop yield and other complementary factors. Data was collected from five non-irrigated field sites in the Waikato region, based on the availability of several years of maize harvest data. To remove potential yield measurement errors and improve the accuracy of spatial interpolation for yield mapping, a customised filtering algorithm was developed. A supervised machine-learning approach for predicting spatial yield was then developed using several prediction models (stepwise multiple linear regression, feedforward neural network, CART decision tree, random forest, Cubist regression, and XGBoost). To provide insights into managing spatiotemporal yield variability, predictor importance analysis was conducted to identify important yield predictors. The spatial filtering method reduced the root mean squared errors of kriging interpolation for all available years (2014, 2015, 2017 and 2018) in a tested site, suggesting that the method developed in R programme was effective for improving the accuracy of the yield maps. For predicting spatial yield, random forest produced the highest prediction accuracies (R² = 0.08 - 0.50), followed by XGBoost (R² = 0.06 - 0.39). Temporal variables (solar radiation, growing degree days (GDD) and rainfall) were proven to be salient yield predictors. This research demonstrates the viability of these models to predict subfield spatial yield, using input data that is inexpensive and readily available to arable farms in New Zealand. The novel approach employed by this thesis may provide opportunities to improve arable farming input-use efficiency and reduce its environmental impact

Interpolation of soil infiltration in furrow irrigation: Comparison of kriging, inverse distance weighting, multilayer perceptron and principal component analysis methods

Study on soil infiltration rate as part of water cycle is essential for managing water resources and designing irrigation systems. The present study was conducted with the aim to compare Kriging, inverse distance weighting (IDW), multilayer perceptron (MLP) and principal component analysis (PCA) methods in the interpolation of soil infiltration in furrow irrigation, and determine the best interpolation method. To conduct infiltration tests, furrows were made on the farm in four triad groups. Infiltration through the blocked furrows method was measured 10, 20, 30, 40, 50, 60, 90, 120, 150, 160, 180 and 210 min after irrigation at a 10-meter distance in each furrow. Data were analyzed by GS+ and Neuro Solutions (NS) software packages. In this study, the maximum error (ME), mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative error (RE) and correlation coefficient (r) were used to compare the interpolation methods. The results of analysis of variance (ANOVA) indicated that differences in methods based on RMSE, MBE, MAE and ME indices were not significant; however, this difference was significant based on r and RE indices. According to the ANOVA results, it can be said that the PCA method with a r of 0.69 and RE of 31%, was predicted with a higher accuracy as compared to other methods