WiFi is starting to be adopted in agricultural settings because of the increasing use of data-centric farming devices and applications. Some of these applications require high data rates that other radio standards cannot offer, for example, realtime yield mapping, drone-based image upload and viewing and video monitoring. It is therefore desirable for farming wireless sensor networks (WSNs) to also utilise available on-farm WiFi networks. However, little information is available on WiFi signal propagation in these environments, as agricultural WSNs have traditionally been based on other radio standards. Therefore, the 2.4GHz WiFi signal propagation characteristics in real outdoor agricultural cropping environments were investigated using infield data loggers. Three distinct farming environments were studied, (i) bare fields, (ii) cotton fields, and (iii) ponded-water rice fields. We studied the effects of (i) weather conditions, (ii) crop growth and (iii) water depth on signal strength across an entire growing season. We also studied the range of reliable data transfer in each environment as a function of height of the logger WiFi antenna above the crop. Crop growth status was found to be much more significant in determining signal strength than weather conditions, with signal strength declining by 8dB over the season in a cotton field, and by 20dB in a rice field. In rice and cotton crops, provided the radios remain 20cm above the crop canopy, ranges in excess of 1km were measured. Significantly greater ranges are predicted if the antenna is more than 40cm above the top of the crop. Regression models were fitted to the measurements to allow predictions and recommendations, with correlation coefficient of determination (R2) values of better than 0.9 in most cases. Radio path loss exponents depended on the environment, and were typically between 3 and 5.

Evaporation from the soil is an important part of the water balance of a crop, when considering water use efficiency. In this paper, a non-intensive method is tested to estimate relative soil evaporation, which is based upon a linear function of soil surface temperature change between a saturated and drying soil. The relative evaporation (RE) method of Ben-Asher et al. (1983) was calibrated using microlysimeters and thermal imaging. Soil surface temperature in a drip irrigated vineyard was then collected using infrared temperature sensors mounted on a quad bike, on several days of the 2009-2010 season. Soil surface temperature in the vineyard ranged from 4.6 °C to 65.5 °C undervine and 6.8 °C to 75.6 °C in the middle of the row. The difference between daily minima and maxima of soil surface temperature ranged from 20.2 °C to 59.7 °C in the inter-row and 13.6 °C to 36.4 °C undervine. Relative evaporation averaged 54% of evaporation from a saturated soil in the inter-row and 97% undervine. Based upon the calculation of RE, the average daily amount of soil evaporation undervine was between 0.64 mm and 1.83 mm, and between 0.69 mm and 2.52 mm inter-row. The soil evaporation undervine and inter-row both exhibited spatial variability across the vineyard, however the undervine area had less spatial variability compared to the inter-row area.

Irrigation scheduling Decision Support Systems (DSS) have seen poor uptake despite proved usage benefits. The failures of some previous systems with proven model accuracy and water savings ability have been attributed to interface difficulties and inappropriate information for end users. Use of the mobile phone Short Messaging Service (SMS) text messages was trialed as an interface to overcome these difficulties. Irrigation system dripper run time scheduling advice was sent daily to 72 Australian irrigators' mobile phones from a water balance system called IrriSat SMS. Irrigators sent back information on irrigations and rainfall, also via SMS, to update the water balance. This trial showed that a complex, water balance-based, DSS could rely on SMS as the sole interface. All 72 irrigators involved were content to receive messages daily for the entire growing season (200 days). A measure of engagement and utility of the system was determined by those who returned their irrigation and rainfall data, 45 sent in their data all season, 13 for half the season and 14 never sent in any data. Thus we infer that 45 users (63%) found the SMS system of enough utility to use for the whole season. Also, at end of season, 6 of the 13 who had stopped half way through said that in retrospect they wished they had not. Thus overall 80% of irrigators found the system useful. User interview data showed the simplicity of use, advice and the prompting effects of intrusive delivery (phone ringing) were key features in the resultant strong engagement of irrigators. Success also relied on appreciating that irrigators will only use objective decision support advice as one element in a set of decision making tools that include subjective and unquantifiable elements, such as plant appearance. This strong uptake reverses the trend in irrigation decision support which has seen poor uptake of sophisticated systems that produce comprehensive scheduling support but which are, or are perceived to be, complex and time consuming to use. Additionally, high participation rates show that much model input data may be collected from irrigators via SMS so it can be used as a very cheap bi-directional communication channel.

A new agricultural sensor data logging platform (WiField) is described, based on IEEE 802.11 WiFi technology. It is low-cost, low-power, and achieves long (>2km) range communication to on-farm WiFi access points. WiFi is an attractive choice for this application because of the wide range of other devices that increasingly need internet access in farming systems. The WiField devices include interfaces for many sensor types; weather, infrastructure (tank and irrigation water levels), and soil status sensing. The interfaces and example corresponding sensors include SDI-12 (capacitive soil moisture probes), soil tension (matric potential), analog voltage and current, UART (water depth sensing using ultrasonic transducers with a digital interface), RS-422 (integrated weather stations), one-wire (DS18B20 temperature sensors) and pulse (flow meters, wind and rain sensors). It integrates solar charging of rechargeable batteries, or can be run off disposable batteries for at least an entire growing season due to design choices that minimize power consumption. It is designed to upload data to cloud services in real-time. The data is then processed in the cloud and interactive graphs are produced, so multiple users can access up-to-date information in order to make optimized, timely farming decisions. The use of the WiField devices in a cotton farming operation is described, for scheduling irrigations and determining crop water use through the soil profile.

Waterlogging and soil salinisation is widespread in the semiarid, irrigated areas of the world. Subsurface drainage is a useful tool in reducing these effects on crops; however, there has been negative downstream effects of drainage in the salt loads discharged to rivers, lakes, and wetlands. Thus, subsurface drainage in semiarid, irrigated areas needs to balance the demands of providing adequate waterlogging and salinity control while minimizing salt loads. Bilevel drainage, in which shallow drains are placed between deeper drains, is a potential method to meet this required balance. This paper describes the development of an analytical solution to this design approach. A previous potential theory was extended to incorporate multiple series of shallow drains placed between two deep drains. The analytical solution was then applied using the Mathematica software to provide useful information on flow rates and flow lines with varying configurations of deep and shallow drains. The theory was then used to compare spacing and drain flow characteristics between a drainage system with only deep drains and multilevel systems that combine shallow drains with deep drains. A large number of possible configurations of shallow drains between deeper drains exist. For ease of comparison, the concept of "drainage equivalence" was developed, representing the drainage discharge per unit spacing between drains. The analytical solution for bilevel drainage situations with single and multiple shallow drains between deeper drains showed that for equivalent rates of total drainage, spacing between deep drains could be increased significantly by the use of shallow drains. It also demonstrated that flow paths and drainage rates from shallow and deep drains and the total system drainage could be altered significantly by altering the number of shallow drains. This information should be useful when considering various drainage configurations to meet the dual objectives of root zone salinity control and minimization of drainage salt loads.

This study integrates measured soil moisture sensor data, a remotely sensed crop vegetation index, and weather data to train models, in order to predict future soil moisture. The study was carried out on a cotton farm, with wireless soil moisture monitoring equipment deployed across five plots. Lasso, Decision Tree, Random Forest and Support Vector Machine modeling methods were trialled. Random Forest models gave consistently good results (mean 7-day prediction error from 8.0 to 16.9 kPA except in one plot with malfunctioning sensors). Linear regression with two of the most important predictor variables was not as accurate, but allowed extraction of an interpretable model. The system was implemented in Google Cloud Platform and a model was trained continuously through the season. An online irrigation dashboard was created showing previous and forecast soil moisture conditions, along with weather and normalized difference vegetation index (NDVI). This was used to guide operators in advance of irrigation water needs. The methodology developed in this study could be used as part of a closed-loop sensing and irrigation automation system.

Increasing water scarcity, higher evapotranspiration rates and reduced rainfall have generated a need for fundamental improvements in irrigation water use efficiency. Evaporation is a significant component of the total evapotranspiration (ET) and high evaporation losses reduce the amount of available water for transpiration, resulting in reduced plant water availability and hence increased irrigation. In order to increase transpiration relative to evaporation and with enhanced pressure on water resources, a reduction in evaporative losses would lead to improved water use efficiency. In order to access the magnitude of soil evaporation losses in semi-arid vineyards and its effects on water use efficiency extensive field trials were carried out to quantify soil evaporation losses. The magnitude of soil evaporation was determined using direct and indirect measurements on a bare soil and in a commercial vineyard.

Soil evaporation is a significant unproductive loss of water that needs to be and can be managed in irrigated systems. A method is used to estimate soil evaporation based upon soil surface temperature change between a saturated and drying soil. The relative evaporation (RE) method of Ben-Asher et al. (1983) was deployed. Soil surface temperature in a drip irrigated vineyard was collected using infra-red temperature sensors. Average daily soil evaporation under-vine was between 0.6mm and 1.8mm and between 0.7mm and 2.5mm for the inter-row. Evaporation from the soil is an important part of the water balance of a crop (Burt et al. 2005). Previous estimates vary widely, from 30-65% of evapotranspiration (Kerridge et al 2008a). The Ben-Asher et al. (1983) method allows potential soil evaporation to be estimated from the daily latent fluxes of a saturated, steady-state dry and a drying soil. By calculating a relative evaporation (RE) factor and multiplying it by an estimate of potential evaporation, determined for example by the FAO-56 procedure (Allen et al., 1998), an estimate of soil evaporation may be made. The main benefit of this method is that it allows rapid and simultaneous estimates of evaporative flux to be measured at numerous sites under study. This can then be linked with methods for spatial estimation of plant water use and stress (Hornbuckle et al., 2008b).