Although New Zealand is water-rich, many of the intensively farmed lowland areas suffer frequent summer droughts. Irrigation schemes have been developed to move water from rivers and aquifers to support agricultural production. There is therefore a need to develop tools and recommendations that consider both water dynamics and outcomes in these irrigated cropping systems. A spatial framework for an existing systems model (APSIM Next Generation) was developed that could capture the variability in soil, cropping systems and irrigation application observed under a single irrigator with constrained water and infrastructure availability. Outputs from the simulations, such as water application, crop stress, yield, drainage and water use efficiency (WUE), could then be produced.The framework was then applied to a case study site, an 80 ha irrigated cropping area in the Hawke’s Bay region of New Zealand.EM and gamma surveys were used to guide a detailed soil survey and to delineate five distinct soil types, with known characteristics such as permeability and water storage. On the site there is a range of soil water storage potential, from 80 to 178 mm of plant available water (PAW), to a depth of 600 mm. A simulation of the study site was created to represent typical management of a maize grain crop. The irrigation scenarios considered were either uniform or variable rate (VRI) application, triggered by a soil water deficit of 40 or 50 % of PAW to 600 mm, and a refill of either 20, 30 or 40% of PAW to 600 mm. The actual trigger or refill point for VRI was determined on a patch basis, while for uniform was determined from the either the soil type with the smallest or greatest PAW, or mean values that proportionally represent the characteristics of the soil types present. It has shown that with the observed variability in soil properties and system constraints, managing uniform irrigation to a single soil type may result in low WUE or yield loss. It has also shown that VRI is comparable to uniform application that is managed by deficits and refill points that proportionally represent the characteristics of all of the soil types present.

Florida has more first magnitude springs that anywhere in the world. Most of these are located in north Florida where agricultural production is the primary basis for the economy. Irrigated corn has become a popular part of the crop rotation in recent years. This project is a study of a corn and peanut rotation investigating Best Management Practices (BMPs) of nitrogen fertility level (336, 246, 157 kg/ha) and irrigation strategies as follows:  (i) GROW, mimicking grower’s practices, (ii) SWB, using a theoretical soil water balance, (iii) SMS, monitoring volumetric water content measured by soil moisture sensors and triggered using maximum allowable depletion (MAD) and field capacity (FC) as thresholds to refill the soil profile, (iv) Reduced: irrigation (60% of GROW) representing a low irrigation treatment and (v) NON: non-irrigated plots. The objectives were to determine the effect on yield of the various treatments as well as nitrogen movement through the profile based on bi-weekly soil samples. During 2015, yield was not significantly different across irrigated treatments; however, the non-irrigated treatment had significantly lower yield than all other treatments except SWB. Fertility rates 336 and 246 kg N/ha, or 246 and 157 kg N/ha were not significantly different; however, the 336 kg N/ha treatment was significantly higher than 157 kg N/ha. Irrigation and fertilizer were reduced without reducing yield by using BMPs compared to conventional practices during the first year of research. Movement of nitrogen through the vadose zone will be discussed.

Weather data has become one of the most widely discussed layers in precision agriculture especially in terms of agricultural ‘big data’. However, most farmers (and even other researchers outside of meteorology) are not likely aware of the complexities required to maintain weather stations that provide data. These stations are exposed to the elements 24/7 and provide unique challenges for sustainment during extreme weather conditions. Based upon decades of experience, this paper discusses data acquisition from loggers and peripheral devices in terms of the network architecture. Numerous methods of quality control/assurance is paramount for detection of failure. Sensors measuring solar radiation, air temperature, relative humidity, wind speed/direction, precipitation, barometric pressure, and soil temperature/moisture are discussed. Once data becomes available, the Kansas Mesonet provides that data to a web-based portal for the public to utilize. Farmers and their advisors are able collect real-time and historic data from the portal via html or an application programming interface (API). Mesonet also integrates this data into agricultural tools critical in assisting with producer decision support. Some examples of these integrations include: evapotranspiration calculations, inversion monitoring, growing degree calculations, freeze monitoring and soil temperature decision tools. 

Soil moisture and temperature are key inputs to several precision agricultural applications such as irrigation scheduling, identifying crop health, pest and disease prediction, yield and acreage estimation, etc.  The existing remote sensing satellites based soil moisture products such as SMAP are of coarse resolution and physics based land surface model such as NLDAS, GLDAS are of coarse resolution as well as not available for real time applications.  Keeping this in focus, we are developing a soil moisture and temperature map for India using high resolution land data assimilation system (HRLDAS) as a computing tool.  The service is aimed to provide soil moisture and soil temperature at 1 km spatial resolution in near real-time (few hours’ latency) at four soil depths and vegetation root zones.  The major highlights in the development of the service are: (1) use of Global Data Assimilation System (GDAS) dataset for dynamic forcing fields, (2) ability to ingest local information about the soil characteristics (3) high resolution USGS land-cover and other static datasets, amongst others.  In this paper, we will focus on modelling set-up details and model evaluation.  Model evaluation is performed against SMAP soil moisture data and local sensors observations using conventional statistics such as MAE, RMSE and correlation coefficient.  The results clearly demonstrate the value of our service in comparison to exiting SMAP data.  In summary, the high resolution soil moisture and soil temperature service that we have developed could be used effectively in a real-time decision support system in precision agriculture.     

Soil moisture/water content monitoring (spatial and temporal) is a critical component of farm management decision primarily for crop/plant growth and yield improvement, but also for optimization of practice such as tillage and field treatments. Satellite humidity probes do not deliver the relevant resolution for farming purposes. Ground moisture probes only provide punctual measurements and do not reflect the true spatial variability of soil moisture.

Previous studies have demonstrated that variations of the nature of the ground/soil (i.e. moisture/water content amongst others parameters) modify the properties of reflected waves resulting in variations of amplitude and phase of the signal-to-noise ratio (SNR) recorded by a Global Navigation Satellite System (GNSS) receiver. Ground based studies analyzing the time variations of SNR measurements linked to dielectric constant of the surrounding soil established a method to recover local fluctuations of soil moisture content.

This method was adapted to airborne application and trialed on low level flying GyroLAG’s light and affordable aircraft also used for other airborne remote sensing agri-surveying work (e.g. magnetic and gamma spectrometry soil zoning). Data processing chain was adjusted to the specifics of the airborne dynamic context. Spatial resolution of 45 m was achieved for the specific test conditions (120-140 km/hr flying speed and 50-100 m agl survey height). Comparison with isolated in situ measurements of soil moisture proved to be poor at less than 75% correlation. A second test is now planned to be performed over a single farm/pivot with validation over a regular grid of in situ measurements of soil moisture.

Soil moisture is one of the most important parameters in precision agriculture. While techniques such as remote sensing seems appropriate for moisture monitoring over large areas, they generally do not offer sufficiently fine resolution for precision work, and there are time restrictions on when the data is available. Moreover, while it is possible to get high resolution-on demand data, but the costs are often prohibitive for most developing countries.

Direct ground level measurement can be a viable and economical alternative if one is able to accurately estimate the value of soil moisture over the entire field by using measurement from only a few points. If the number of measurement points, their location, and data are available, then Compressive Sensing (CS) theory may be used to give an estimate of the moisture. This is because although moisture values in a field do not constitute a sparse signal, they are spatially correlated and can be expressed as sparse signals in other domains such as DCT or DFT.

The difficulty in using the CS theory for estimation of moisture values is that the number and location of the sensors must be known a priori.  In reality, this means the optimization problem has to be solved several times for various different network configurations to determine the best layout. Straightforward augmentation of the CS reconstruction optimization problem to include the configuration selection leads to so called MINLP optimization type of problems which are combinatorial and non-polynomial time. Such problems take exceptionally large times to solve for large scale problems (as encountered in PA type of applications).

 In this paper, we propose a new heuristic algorithm to find a sub-optimal set for sensor locations. A data set for numerical experiments is extracted from the simulation of a simple field using the state-of-the-art TIN-based Real-time Integrate Basin Simulator (tRIBS). This data set is used for validation of the optimization results