Soil Water Sensors Research Articles

Simple soil water monitoring tools increase yield and income of smallholder farmers in Malawi: A case study of four irrigation schemes

AbstractThe area under irrigation in Malawi has increased, but productivity is still low due to farmers' lack of irrigation knowledge. This paper describes a ‘people‐centred learning’ approach aimed at improving water management in smallholder irrigation schemes. The work was conducted in Malawi, where farmers at the Bwanje, Tadala, Nanzolo and Matabwa irrigation schemes were provided with soil water sensors that showed whether the soil was wet, moist or dry using colours, making it easy for farmers to understand. Indicators related to water management and soil water status were monitored during the 2017 and 2018 irrigation seasons. The yield and gross margins were then assessed. The results show that farmers can effectively identify irrigation‐related problems and act on the information provided by the tools. The yield increased by more than 80%, and the gross margins increased by more than threefold across all the schemes. Based on the findings outlined, the use of soil water sensors enhances farmers' understanding of water management, prompting behavioural changes and measurable improvements in irrigation productivity. Furthermore, this study concludes that improving farmers' knowledge is essential for transforming smallholder irrigation schemes into profitable and sustainable investments.

Improving vineyard irrigation efficiency with soil water sensors

Improving vineyard irrigation efficiency with soil water sensors Using soil water sensors to automatically schedule irrigation in winegrape vineyards offers the potential for growers to enhance water use productivity under warmer climate conditions. The use of electrical soil water sensors for improving crop water use efficiency is an emerging trend in agricultural regions of the world where irrigation is vital for sustainable food production. This advancement uses solar powered electronic capacitance probes to activate irrigation in real time for maintaining a desired level of soil water content needed by a particular crop.

Metrics for Evaluating Interreplicate Variability of Irrigation Scheduling Sensors

Highlights Lower variability among replicates is associated with greater reliability for recommending irrigation timing. Eight metrics were presented for variability comparisons independent of sensor types and calibration accuracy. One neutron thermalization type tended to be less variable than six dielectric types in a comparison at 0.3 m depth. One granular matrix type tended to be less variable than one dielectric type in a comparison across the top 1 m of soil. Abstract. Much of the research on irrigation scheduling sensors, especially soil water sensors, assesses and refines the accuracy of sensor calibrations. However, a sensor with an accurate calibration but high variability among replicates may require a larger-than-acceptable number of replicates for informing recommendations of optimal irrigation timing. To compare the interreplicate variability of sensors across types and calibration accuracy levels, this study presented eight metrics: (1) absolute spread-to-change ratio, (2) shifted spread-to-change ratio, (3) coefficient of change variation, (4) standard deviation of relative value, (5) standard deviation of relative change, (6) standard deviation of absolute triggering date, (7) standard deviation of shifted triggering date, and (8) standard deviation of relative triggering date. These metrics enabled comparisons either by nondimensionalizing sensor measurements or by expressing interreplicate variability in terms of time. For demonstrating their usage and their particularities, the metrics were applied to two datasets that included soil water sensor types such as neutron probe (503DR), dielectric sensor (TDR-315, CS616, CS655, HydraProbe II, 5TE, TEROS 12, Drill & Drop), and granular matrix sensor (Watermark 200SS). The neutron probe in the single-depth dataset and the granular matrix sensor in the multi-depth dataset generally displayed less interreplicate variability than other evaluated sensor types over multiple drying cycles. Future research is suggested to calculate and improve the eight metrics for identifying combinations of sensor types, deployment methods, and data interpretation techniques that minimize interreplicate variability and maximize irrigation scheduling precision. Keywords: Assessment, Comparison, Index, Nondimensionalization, Precision, Reliability, Soil water, Standardization, Uncertainty, Variation.

IMPROVING CROPS BY IRRIGATION MANAGEMENT AND SOIL CONDITIONERS: A REVIEW

Deficit irrigation strategies would require an accurate assessment of growth stage-specific stress tolerances for vegetable crops and optimal water management supported by advanced irrigation systems; i.e., able to promptly cope with crop water requirements at sensitive phenological stages However, in semiarid environments, the fulfillment of leaching requirements significantly limits the possibility of applying deficit irrigation criteria and reduces the WUE in irrigated vegetable production. The adoption of methods to control the level of plant water stress (using soil tensiometers or other soil water sensors or calculation of water balance) can reduce the amount of water applied to the crop up to 53%. Use of subsurface drip irrigation has progressed from being a novelty used only in experimental fields to an accepted method of irrigation for both tree and vegetable crops mentioned that subsurface drip irrigation on lettuce, tomato and sweet corn (Z. mays) has significantly increased yield and WUE in all these crops. Subsurface drip irrigation may increase WUE in semiarid environment under saline conditions by increasing yield. Our research goal is to suggest a framework could be utilized from natural and synthetic materials that could be used as soil and/or plant conditioners to maximize water use efficiency under drought conditions

Effective irrigation scheduling to improve corn yield, net returns, and water use

AbstractIrrigated cropland continues to increase in Tennessee to sustain yield when seasonal temperatures are high and water deficit occurs. Corn (Zea mays L.) production is directly related to water availability. Inefficient irrigation scheduling can result in excessive or inadequate water applications. The objective of this study was to optimize profitable corn yield using soil water sensors to improve irrigation scheduling. Three irrigation regimes plus a rainfed/check were evaluated in a field study at the University of Tennessee Milan Research and Education Center in 2020 and 2021. Soil water level was monitored with Meter Inc. Teros 21 matric potential sensors and ZL6 soil‐water loggers. Irrigation‐scheduling decisions were based on rainfall, plant growth stages, and soil water sensors. An optimal irrigation schedule was determined within this study that increased yield by 13.0% and 3.0%, respectively, in 2020 and 2021 while also having the lowest cost among irrigation treatments. Results indicate that irrigation scheduling can improve yield, conserve water, and increase economic returns.

Toward automated irrigation management with integrated crop water stress index and spatial soil water balance

AbstractDecision support systems intended for precision irrigation aim at reducing irrigation applications while optimizing crop yield to achieve maximum crop water productivity (CWP). These systems incorporate on-site sensor data, remote sensing inputs, and advanced algorithms with spatial and temporal characteristics to compute precise crop water needs. The availability of variable rate irrigation (VRI) systems enables irrigation applications at a sub-field scale. The combination of an appropriate VRI system along with a precise decision support system would be ideal for improved CWP. The objective of this study was to compare and evaluate two decision support systems in terms of seasonal applied irrigation, crop yield, and CWP. This study implemented the Spatial EvapoTranspiration Modeling Interface (SETMI) model and the Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system for management of a center pivot irrigation system in a 58-ha maize-soybean field during the 2020 and 2021 growing seasons. The irrigation scheduling methods included: ISSCADA plant feedback, ISSCADA hybrid, common practice, and SETMI. These methods were applied at irrigation levels of 0, 50, 100, and 150% of the full irrigation prescribed by the respective irrigation scheduling method. Data from infrared thermometers (IRTs), soil water sensors, weather stations, and satellites were used in the irrigation methods. Mean seasonal irrigation prescribed was different among the irrigation levels and methods for the 2 years. The ISSCADA plant feedback prescribed the least irrigation among the methods for majority of the cases. The common practice prescribed the largest seasonal irrigation depth among the methods for three crop-year cases. The maize yield in rainfed was found to be significantly lower than the irrigated levels in 2020 since 2020 was a dry year. No significant differences were observed in crop yield among the different irrigation methods for both years. The CWP among the different irrigation methods ranged between 2.72 and 3.15 kg m−3 for 2020 maize, 1.03 and 1.13 kg m−3 for 2020 soybean, 3.57 and 4.24 kg m−3 for 2021 maize, and 1.19 and 1.48 kg m−3 for 2021 soybean. Deficit level (50%) had the largest irrigation water productivity in all crop-year cases in this study. The ISSCADA and SETMI systems were found to reduce irrigation applications as compared to the common practice while maintaining crop yield. This study was the first to implement the newly developed integrated crop water stress index (iCWSI) thresholds and the ISSCADA system for site-specific irrigation of maize and soybean in Nebraska.

Soil water sensors for irrigation scheduling in the United States: A systematic review of literature

Soil water sensors for irrigation scheduling in the United States: A systematic review of literature

Performance of a Multi-Sensor Capacitance Probe in Estimating Soil Water Content and Field Capacity

Highlights Among six manufacturer calibrations, the default calibration resulted in the largest errors. Sensor performance was negatively affected by higher clay content and salinity. Sensor-based approaches to estimating field capacity were inconsistent and spatially variable. Abstract. Maintaining the economic and environmental sustainability of crop production requires optimizing irrigation management using advanced technologies such as soil water sensors. In this study, the performance of a commercially available multi-sensor capacitance probe was evaluated under irrigated field conditions across western Oklahoma. The effects of clay content and salinity on sensor performance were investigated too. In addition, the field capacity (FC) of soil cores collected at study sites was determined in the laboratory. These laboratory FC values were used to assess the performance of two sensor-based approaches for estimating FC: the days to reach laboratory FC after major watering events and the percentile of collected sensor readings that represented laboratory FC. The results showed that among the six calibrations provided by the manufacturer, the default and silty clay loam calibrations produced the largest and smallest soil water content errors, respectively. Errors generally increased with clay and salinity, except for the heavy clay calibration, which showed improved performance with increasing clay content. The default and sand calibrations were more sensitive to increases in clay and salinity compared to other calibrations. In the case of sensor-based FC, on average, one to three days were required to reach laboratory FC, with a large range of one to nine days. The percentiles representing laboratory FC had an average of 56% and a range of 3%-97%. Overall, the sensor-based approaches produced inconsistent and highly variable estimates of FC. Keywords: Calibrations, Clay content, Irrigation scheduling, Salinity, Sensor accuracy, Soil water threshold.

Field Evaluation of Conventional and Downhole TDR Soil Water Sensors for Irrigation Scheduling in a Clay Loam Soil

Highlights Soil profile water content derived from Acclima TDR-315™ sensors approximated those from NMM measurements. Soil profile water content from Campbell Scientific SoilVUE™10 sensors grossly underestimated those from the NMM. VWC values from SoilVUE10 sensors were consistently less than those reported by the TDR-315 sensors at all depths. These findings do not support SoilVUE10 use for irrigation scheduling in clay loam soils. Abstract. A field study was performed to evaluate the efficacy of two commercially available time domain reflectometry (TDR) soil water sensors for irrigation scheduling in a clay loam soil near Bushland, Texas. SoilVUE10 (Campbell Scientific Inc., Logan, Utah) and TDR-315 (Acclima Inc., Meridian, Idaho) sensors were installed within 30 cm of neutron moisture meter (NMM) access tubes in a research field planted to corn (Zea mays L) in 2020 and irrigated by a center pivot sprinkler system. Irrigation treatments included 50%, 75%, and 100% of evapotranspiration (ET) replacement with two access tubes installed in each plot, totaling six sensor evaluation sites. Semiweekly measurements with a field-calibrated NMM were used to monitor soil water status and schedule irrigation throughout the growing season. Soil profile water content values integrated over the surface to 1.1-m depth range were derived from SoilVUE10 and vertically distributed arrays of Acclima TDR-315 sensors installed at equivalent depths and were compared with those from NMM data. Average profile soil water contents from the TDR-315 sensors trended well with those from the NMM having mean bias difference (MBD) values of -9.8, -3.1, and 8.4 mm for the 50%, 75%, and 100% treatments, respectively. In contrast, soil profile water content values from the SoilVUE10 sensors grossly underestimated those from the NMM for all irrigation treatments with MBD values of -54.4, -70.5, and -89.8 mm for the 50%, 75%, and 100% treatments, respectively. Comparisons of volumetric water content (VWC) at each of the nine depths common to both electromagnetic sensor types revealed that values from the SoilVUE10 sensors were consistently less than TDR-315 values for all irrigation treatments. Underestimation at the near surface (5 and 10 cm depths) was attributed to loss of soil to electrode contact possibly associated with clay shrinkage during periodic drying following irrigation. Although soil to electrode contact can be problematic at greater depths, the explanation for chronic underestimation of VWC was less obvious except to note that underestimation occurred immediately after installation, which indicated poor electrode-soil contact after installation despite use of manufacturer guidelines and tools. Other possible reasons include challenges for accurate estimation of soil permittivity for a measured permittivity that includes the plastic sensor body. Results from this study suggest vertically distributed arrays of TDR-315 sensors can provide profile water content values adequate for monitoring soil water status for irrigation scheduling in a clay loam soil. The chronic underestimation observed for the SoilVUE10 sensors does not support their use for water resources research and irrigation management and could lead to over irrigation. Additionally, the relatively short 1 m length is less than the rooting depth of many regional crops and thus not capable of determining percolation below the root zone. Keywords: Acclima TDR-315, Campbell Scientific SoilVUE, Irrigation scheduling, Neutron moisture meter, Semi-arid, Soil water sensors, Time domain reflectometry, Volumetric water content.

Forecasting of Canopy Temperatures Using Machine Learning Algorithms

Highlights This study analyzes the feasibility of using Artificial Neural Networks (ANNs) to estimate canopy temperatures. A methodology is introduced to forecast canopy temperatures using historical canopy temperatures. ANNs can predict canopy temperatures with satisfactory accuracy for plant stress-based irrigation scheduling. The methodology can be useful to add redundancy to plant stress-based irrigation scheduling methods. Abstract. Recent advances can provide farmers with irrigation scheduling tools based on crop stress indicators to assist in the management of Variable Rate Irrigation (VRI) center pivot systems. These tools were integrated into an Irrigation Scheduling Supervisory Control and Data Acquisition System (ISSCADAS) developed by scientists with the USDA-Agricultural Research Service (ARS). The ISSCADAS automates the collection of data from a network of wireless infrared thermometers (IRTs) distributed on a center pivot’s lateral and in the field irrigated by the center pivot, as well as data from a wireless soil water sensor network and a microclimate weather station. This study analyzes the use of Artificial Neural Networks (ANNs), a type of machine learning algorithm, for the forecasting of canopy temperatures obtained by a wireless network of IRTs mounted on a three-span VRI center pivot irrigating corn near Bushland, TX, during the summer of 2017. Among the predictors used by the ANNs were weather variables relevant to the estimation of evapotranspiration (i.e., air temperature, relative humidity, solar irradiance, and wind speed), irrigation management variables (irrigation treatment, irrigation scheduling method, and the amount of water received during the last 5 days as irrigation or rainfall), and days after planting. Two case studies were conducted using data collected from periodic scans of the field performed during the growing season by running the pivot dry. In the first case, data from the first three scans were used to train an ANN, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the fourth scan. In the second case, data from the first six scans were used to train ANNs, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the seventh scan. The Root of the Mean Squared Error (RMSE) of ANN predictions in the first case ranged from 1.04°C to 2.49°C, whereas the RMSE of ANN predictions in the second case ranged from 2.14°C to 2.77°C. To assess the impact of ANN accuracy on irrigation management, estimated canopy temperatures were fed to a plant-stress-based irrigation scheduling method, and the resulting prescription maps were compared against prescription maps obtained by the same method using the canopy temperatures measured by the network of IRTs. In the first case, no difference was found between both prescription maps. In the second case, only one plot (out of 26) was assigned a different prescription. Results of this study suggest that machine learning techniques can be used to assist the ISSCADAS in situations where canopy temperatures cannot be measured by the network of IRTs due to poor visibility conditions, or because the center pivot cannot traverse the field within a reasonable amount of time. Keywords: Artificial Neural Network, Canopy temperature sensing, Center pivot irrigation, Irrigation scheduling, Machine learning, Metamodeling, Variable rate irrigation.

Field evaluation of automated site-specific irrigation for cotton and perennial ryegrass using soil-water sensors and Model Predictive Control

Variable-rate irrigation technology can reduce water use in centre pivot and lateral move irrigation systems through application of irrigation according to spatially varied soil-water profiles. However, filling the profile may not maximise yield because of variations in crop response and water requirements with crop stage. For example, cotton crops produce optimal yield under slight water stress during early stages. An irrigation strategy ‘Model Predictive Control’ has been implemented that accounts for changes in crop water requirements at different growth stages using biophysical crop models. This strategy involves automatically and iteratively executing the biophysical crop model APSIM parameterised with local soil and weather information, with different irrigation depths, to identify which combination maximises yield with the minimum depth of water application. This strategy has potential to address spatial and temporal variations in crop water requirements but has not previously been evaluated for variable-rate irrigation in the field. This paper reports field trials conducted over four cotton (Gossypium hirsutum L.) seasons and two perennial ryegrass (Lolium. perenne L.) seasons to evaluate the accuracy of the yield prediction of the biophysical model and compare field performance of irrigation strategies: uniform irrigation and variable-rate irrigation using a fixed underlying map, soil-water sensors and Model Predictive Control. Yield was most accurately predicted using on-site weather data and field soil core information, with R² = 0.733 and RMSE = 153.9 kg/ha for cotton, and R² = 0.336 and RMSE = 295.3 kg/ha for ryegrass. For cotton, Model Predictive Control led to 4.9% more yield with 5.6% reduction in water application, mainly through reduced water after peak bloom and/or open boll physiological stages. For grazed ryegrass, the Model Predictive Control strategy led to 8.5% more yield with 5.4% reduction in water application, potentially caused by reduced applications after grazing events. Further work includes evaluating the Model Predictive Control strategy with control of irrigation event timing under a broader range of field conditions to identify parameters to provide greatest economic return, and to refine biophysical models for improved performance of optimisation in the strategy.

Methods for downhole soil water sensor calibration—Complications of bulk density and water content variations

AbstractDownhole soil volumetric water content (VWC) sensors are used in access tubes to assess the soil water content at multiple depths. If sensor readings are spaced closely enough vertically and are accurate enough, then accurate soil profile water content storage and change in storage can be determined over the depth range of readings, leading to accurate estimates of evapotranspiration (ET) if readings extend to well below the root zone. Even if sensing only covers the active root zone, soil water depletion may be determined well enough to inform irrigation scheduling. While sensor accuracy is dependent on many factors, including the sensor's physical principle of operation, soil‐specific calibration is typically required for good accuracy. In soils with multiple horizons (layers) of different texture, bulk density (BD), or chemical composition, horizon‐specific calibrations may be necessary. We describe methods and equipment used for downhole sensor calibration to typical accuracy of <0.01 m3 m−3 with specific reference to calibration of 10 neutron scattering meters in a soil that required three different horizon‐specific calibrations. Our results contrast with the factory calibration, which would result in a 38‐mm error in water stored in a 1.5‐m deep profile of our soil. We describe variability of measured VWC and BD with depth, distance, and water content and the errors that result from using BD to convert mass basis (g g−1) water content data to VWC data, which can be as much as 35 mm (7.26% underestimation) for soil water storage in a 1.5‐m deep profile of our soil.

A Novel Portable Soil Water Sensor Based on Temperature Compensation

Soil water sensors based on the standing wave rate (SWR) principle are affected by temperature in long-term operation. To address this problem, a temperature compensation model based on the binary regression analysis method is proposed. The measurement results of the temperature-compensated standing wave rate (TCSWR) sensor at different temperatures and soil volumetric water content are analyzed, and the least-squares principle is used to identify the parameters to be determined in the compensation model for temperature for the SWR soil water sensor. A portable tapered TCSWR sensor with built-in temperature compensation model was developed on this basis. The calibration results show that the standing wave measurement circuit of the TCSWR sensor can effectively respond to changes in soil water, and the coefficient of the fitted equation exceeds 0.95. A comparison of the results before and after temperature compensation proves that compensation can significantly reduce the measurement error of the TCSWR sensor and improve the measurement accuracy. The static and dynamic characteristics of the TCSWR sensor show that the measurement range of the TCSWR sensor is7.50%-31.50%, the measurement accuracy is ±0.63%, the stability is good, the resolution is a minimum of 0.05%, and the dynamic response time is less than 1 s. The absolute error of the TCSWR sensor measurement is less than 1% in comparison with similar sensors, demonstrating that the measurement results of the TCSWR sensor are reliable.

Design of automatic control system on trickle irrigation for tomato cultivation

The development of technology has had an impact on the progress of irrigation systems, called the intelligent irrigation system 4.0. The objectives of this research are a measurement of Emission Uniformity (EU) and designing automatic irrigation based on soil water sensors. The result of EU value is in the range of 85-90%, which is a good category. EU value can determine whether one emitter can represent the entire emitter in one irrigation system. The principle of the soil water sensor is to read the value of the soil moisture content in the planting medium. The soil water content of 22.4%, which is the field capacity, is used as an upper limit and the soil water content of 21.1%, which is the critical point, is used as the lower limit. Determination of the critical point value is done by using the management allowable depletion (MAD) value of 0.52. The working principle of the control system is to monitor the work of the soil water sensor and become the main data to control valves, pumps, and dosing. The required cost for the automatic system is Rp. 12,626,000, -.

Sensors and frequencies of soil water content measurement affecting agro-hydrological simulations and irrigation management

• The frequency of observation of soil water content affected the performance of inverse modeling. • Soil Hydraulic Properties (SHP) differed according to the water sensor used. • Differences observed in SHP affected the obtaining of important parameters for irrigation management. Principles and frequency of soil water content (SWC) measurements used in the transient water flow experiments may cause distinction on the determination of soil hydraulic properties (SHP) by inverse modelling (IM). Consequently, use of agro-hydrological models and irrigation management can be affected. This work has the following objectives: (i) to analyze if the type of sensor and the frequency of SWC measurement used in the transient water flow experiments for IM affect the results regarding SHP; (ii) to assess if variations on the SWC measurement frequencies affect the results of SHP to the point of causing effects on the agro-hydrological modelling through SWAP model and on the passion fruit irrigation management based on soil water sensing; (iii) point out strategies for the correct irrigation management based on SHP obtained by IM. The results regarding SHP were applied to the modelling of crop evapotranspiration (ETc) and SWC with the use of SWAP agro-hydrological model. The type of sensor and the frequency of SWC data measurements interfered in the results regarding SHP obtained by IM, with implications on the agro-hydrological modelling and on the irrigation management through soil water sensing (SWS). The results of this study alert for the differences that may occur in SWC values when those are obtained from matric potential through soil water retention curves. This is specially important for higher and lower critical matric potentials used as reference for the moment to turn on and off a irrigation. When using GS1 sensors for IM, it is recommended to use intervals of SWC data measurement of 1 h, as the TDR sensors performed better with SWC data acquisition frequency between 1 and 12 h. Finally, it is understood that the factors that affect agro-hydrological modeling and the performance of the type of soil water sensor are diverse and integrated in a complex nonlinear way, for instance: atmospheric condition, soil texture and structure, topography, presence/absence of plants, soil and irrigation management practices. Thus, similar studies should be carried out considering variations in these factors so that a generalist and integrated conclusion is possible.

(INVITED)Advances in fiber optic sensors for soil moisture monitoring: A review

Real time measurements of soil water content play a critical role in many fields of science as agronomy, geology, engineering, and hydrology. In the last years, agriculture has become one of the most important application fields of soil water sensors technology in order to optimize the irrigation process and to guarantee sustainable water resources management. This work provides a review on the latest emerging methodologies based on optical fiber sensing for soil moisture monitoring for agricultural and hydrological applications. In particular, the main studies referring to optical fiber sensors based on a variation of the refractive index of the external medium, sensors based on heated distributed temperature sensor (HDTS) and sensors based on Fiber Bragg Gratings (FBG) or Long Period Gratings (LPG) are here explored. Finally, some approaches based on the NIR absorbance spectroscopy are proposed for measuring the soil water content. Most of these approaches and technologies are still in a prototyping phase and only few of them are properly evaluated in situ real context.

Soil water and stable isotopes in the unsaturated zone profile – field observations and analysis

In this review, we provide research data emphasizing the role of groundwater table and hydrometeorological parameters variation on soil water fluctuations in the unsaturated soil profile. We focused on soil water content, air temperature, precipitation and groundwater table observations. The ThetaProbe Soil Water Sensors basing on changes in the apparent dielectric constant were used for soil volumetric water content measurements. The sensors were installed at the different depths of the unsaturated zone profile under natural atmospheric conditions. The observations lasted more than five years (14 November 2013 – 31 December 2018). Groundwater table data was obtained using Diver data loggers, which continuously recorded water pressure and temperature. For more detailed investigations of water and mass transport at the Maišiagala radioactive waste repository site water, stable isotopes as environmental tracers were used as well.

Sensor placement in 2D/3D wetting patterns from drip irrigation for quantification of evapotranspiration

Variability of water extraction in the root zone is a difficulty in determining criteria for sensor placement aimed at soil water balance and irrigation management. On the other hand, detailed knowledge on the water extraction variability can be the key point to define criteria for sensor placement and put an end to arbitrary decisions. The objective of this study was: (i) to evaluate the influence of sensor placement in one, two and three-dimensional space on the determination of passion fruit evapotranspiration by the soil water balance method (SWB); (ii) develop a placement criterion for soil water sensor (SWS) installation to monitor soil water content (θ) for determining SWB with precision and accuracy close to obtained in a lysimeter-grown passion fruit plant. Passion fruit was cultivated in a protected environment and its evapotranspiration (ETc) was measured by a weighing lysimeter installed in the center of the orchard, using a weighing platform. Inside the lysimeters, 32 TDR sensors were arranged in different positions in order to form one (1D), two (2D) and three-dimensional (3D) monitoring of θ in the root zone. ETc determined by SWB in 2D/3D was similar to the ETc determined by lysimetry. It possible to identify a specific location that can represent the average water extraction within the wet bulb zone or in the root zone, for placing or actuating sensors for irrigation management. In the specific case of this experiment, for example, it was found that the installation of a single probe between 0 and 0.3 m depth and at 0.10 m distance from the dripper allowed obtaining ETc values close to those obtained by weighing lysimeter. The results of this study represent another step in the search for developing general criteria for SWS positioning, which is particularly useful and necessary to make drip irrigation management accurate. However, considering that the study was carried out under a highly controlled condition, with a specific crop, cultivated in a protected environment, with a consistent irrigation management criterium, future studies are necessary for the implementation of generalist criteria.

Evapotranspiration in young lime trees with automated irrigation

Citrus latifolia is increasingly being cultivated in Mediterranean areas, where the need for precise irrigation implies a sound knowledge of the crop's water requirements, which is carried out by monitoring the tree water status in the plant-soil-atmosphere system. Two year-old lime trees (Citrus latifolia Tan. cv. Bearss) were cultivated in 45 L weighing pot-lysimeters filled with a clay-loam soil and equipped with soil water sensors for recording the real-time soil water status. Irrigation, drainage, pot weight, and agro-meteorological variables were also recorded in real-time. Plant water relations - stem (Ψstem) and leaf (Ψleaf) water potentials, and leaf gas exchange - were periodically measured throughout the experiment. The automated irrigation protocol based on the volumetric soil water content was set to prevent tree water stress (with mean seasonal values of Ψstem above -0.8 MPa and 7 μmol m−2 s−1 and 80 mmol m−2 s−1, for net phtosynthesis and stomatal conductance, respectively). From the soil water pot balance, the computed fortnightly ET mean values throughout the growing season varied from 0.25 to 2.56 L plant−1 day−1 in winter and summer months, respectively. Maximum ET values occurred in July when evaporative demand was highest and lime fruits were in their most active growing phase. For day length, lime tree biomass changes were negligible, and the pot tree weight variations served for ET water balance validation (r2=0.92***). The contribution of this work is that it establishes a scalable weighing lysimeter prototype for determining the ET of lime trees grown in Mediterranean semi-arid conditions, using real-time soil water balance monitoring managed by an irrigation automation protocol. This approach could be replicated for precise ET measurements in other crops.

Effects of Nitrogen Rate and Cover Crop on Cotton (Gossypium hirsutum L.) Yield and Soil Water Content

The objective of this study was to test the effects of N rates and tillage radish (Raphanus sativus var. longipinnatus) cover crop (TRCC) on soil water and cotton (Gossypium hirsutum L.) yield. In three years of the investigation, the treatments were N rates at 84 kg ha−1 and 140 kg ha−1 with and without TRCC. Soil water contents were measured using soil water sensors. Results showed that cotton yield was not significantly (p > 0.05) influenced by TRCC. Compared to N rate at 84 kg ha−1, 140 kg N ha−1 increased lint yield by 2.0%, 7.4%, 18.4% in 2017, 2018, and 2019, respectively, but the increase was significant only in 2019 (p < 0.02). Interactions between TRCC and nitrogen rate on yield were significant (p < 0.03) only in 2017. TRCC increased soil water infiltration capacity, resulting in higher soil water content. Use of TRCC did not affect the cotton yield, which could be due to the high inputs of water and high rates of N neutralizing the positive contributions to the cotton growth expected from the TRCC. Sub-optimum winter temperatures hampered the establishment and subsequent growth of TRCC, which also possibly contributed to its minimum impacts on cotton crop performance in the following season.