Comparison of Sensor-Based and Weather-Based Irrigation Scheduling

2020 ◽  
Vol 36 (3) ◽  
pp. 375-386
Author(s):  
Ruixiu Sui ◽  
Earl D. Vories
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Texture ◽  
Irrigation Scheduling ◽  
Cotton Field ◽  
List Type ◽  
Soil Moisture Sensors

HighlightsSensor-based irrigation scheduling methods (SBISM) were compared with computerized water balance scheduling.Number and time of irrigation events scheduled using the SBISM were often different from those predicted by the computerized method.The highly variable soils at the Missouri site complicated interpretation of the sensor values.Both SBISM and computerized water balance scheduling could be used for irrigation scheduling with close attention to soil texture and effective rainfall or irrigation.Abstract. Sensor-based irrigation scheduling methods (SBISM) measure soil moisture to allow scheduling of irrigation events based on the soil-water status. With rapid development of soil moisture sensors, more producers have become interested in SBISM, but interpretation of the sensor data is often difficult. Computer-based methods attempt to estimate soil water content and the Arkansas Irrigation Scheduler (AIS) is one example of a weather-based irrigation scheduling tool that has been used in the Mid-South for many years. To aid producers and consultants interested in learning more about irrigation scheduling, field studies were conducted for two years in Mississippi and a year in Missouri to compare SBISM with the AIS. Soil moisture sensors (Decagon GS-1, Acclima TDR-315, Watermark 200SS) were installed in multiple locations of a soybean field (Mississippi) and cotton field (Missouri). Soil water contents of the fields were measured hourly at multiple depths during the growing seasons. The AIS was installed on a computer to estimate soil water content and the required data were obtained from nearby weather stations at both locations and manually entered in the program. In Mississippi, numbers and times of the irrigation events triggered by the SBISM were compared with those that would have been scheduled by the AIS. Results showed the number and time of irrigation events scheduled using the SBISM were often different from those predicted by the AIS, especially during the 2018 growing season. The highly variable soils at the Missouri site complicated the interpretation of the sensor values. While all of the sites were within the Tiptonville silt loam map unit, some of the measurements appeared to come from sandier soils. The AIS assumed more water entered the soil than the sensors indicated from both irrigations and rainfalls less than 25 mm. While the irrigation amounts were based on the pivot sprinkler chart, previous testing had confirmed the accuracy of the charts. Furthermore, the difference varied among sites, especially for rainfall large enough to cause runoff. The recommendations based on the Watermark sensors agreed fairly well with the AIS in July after the data from the sandiest site was omitted; however, the later irrigations called for by the AIS were not indicated by the sensors. Both the sensor-based irrigation scheduling method and the AIS could be used as tools for irrigation management in the Mid-South region, but with careful attention to soil texture and the effective portion of rainfall or irrigation. Keywords: Irrigation scheduling, Soil moisture sensor, Soil water content, Water management.

scholarly journals Irrigation Scheduling Using Soil Moisture Sensors

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Ruixiu Sui
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Management Practices ◽  
Root Zone ◽  
Weighted Average ◽  
Irrigation Scheduling ◽  
Time Interval ◽  
Soil Moisture Sensors

Irrigation is required to ensure crop production. Practical methods of use sensors to determine soil water status are needed in irrigation scheduling. Soil moisture sensors were evaluated and used for irrigation scheduling in humid region of the Mid-South US. Soil moisture sensors were installed in soil at depths of 15 cm, 30 cm, and 61 cm belowground. Soil volumetric water content was automatically measured by the sensors in a time interval of an hour during the crop growing season. Soil moisture data were wirelessly transferred onto internet through a wireless sensor network (WSN) so that the data could be remotely accessed online. Soil water content measured at the three depths were interpreted using a weighted average method to reflect the status of soil water in plant root zone. A threshold to trigger an irrigation event was determined with sensor-measured soil water content. An antenna mounting device was developed for operation of the WSN. Using the antenna mounting device, the soil moisture measurement was not be interrupted by crop field management practices.

scholarly journals Soil Structure and Texture Effects on the Precision of Soil Water Content Measurements with a Capacitance- Based Electromagnetic Sensor

2020 ◽  
Vol 63 (1) ◽  
pp. 141-152
Author(s):  
Jasreman Singh ◽  
Derek M. Heeren ◽  
Daran R. Rudnick ◽  
Wayne E. Woldt ◽  
Geng Bai ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Sensor Calibration ◽  
Soil Water Depletion ◽  
Undisturbed Soil ◽  
Water Depletion ◽  
Soil Moisture Sensors ◽  
Electromagnetic Sensor

HighlightsCapacitance-based electromagnetic soil moisture sensors were tested in disturbed and undisturbed soils.The uncertainty in estimation of soil water depth was lower using the undisturbed soil sample calibrations.The uncertainty in estimation of soil water depletion was lower than the uncertainty in volumetric water content.Undisturbed calibration of water depletion quantifies water demand with better precision and avoids over-watering.Abstract. The physical properties of soil, such as structure and texture, can affect the performance of an electromagnetic sensor in measuring soil water content. Historically, calibrations have been performed on repacked samples in the laboratory and on soils in the field, but little research has been done on laboratory calibrations with intact (undisturbed) soil cores. In this study, three replications each of disturbed and undisturbed soil samples were collected from two soil texture classes (Yutan silty clay loam and Fillmore silt loam) at a field site in eastern Nebraska to investigate the effects of soil structure and texture on the precision of a METER Group GS-1 capacitance-based sensor calibration. In addition, GS-1 sensors were installed in the field near the soil collection sites at three depths (0.15, 0.46, and 0.76 m). The soil moisture sensor had higher precision in the undisturbed laboratory setup, as the undisturbed calibration had a better correlation [slope closer to one, R2undisturbed (0.89) > R2disturbed (0.73)] than the disturbed calibrations for the Yutan and Fillmore texture classes, and the root mean square difference using the laboratory calibration (RMSDL) was higher for pooled disturbed samples (0.053 m3 m-3) in comparison to pooled undisturbed samples (0.023 m3 m-3). The uncertainty in determination of volumetric water content (?v) was higher using the factory calibration (RMSDF) in comparison to the laboratory calibration (RMSDL) for the different soil structures and texture classes. In general, the uncertainty in estimation of soil water depth was greater than the uncertainty in estimation of soil water depletion by the sensors installed in the field, and the uncertainties in estimation of depth and depletion were lower using the calibration developed from the undisturbed soil samples. The undisturbed calibration of soil water depletion would determine water demand with better precision and potentially avoid over-watering, offering relief from water shortages. Further investigation of sensor calibration techniques is required to enhance the applicability of soil moisture sensors for efficient irrigation management. Keywords: Calibration, Capacitance, Depletion, Irrigation, Precision, Sensor, Soil water content, Structure, Uncertainty.

scholarly journals Drought stress tolerance of two wheat genotypes

2008 ◽  
Vol 3 (Special Issue No. 1) ◽  
pp. S95-S104 ◽  
Author(s):  
A. Lukács ◽  
G. Pártay ◽  
T. Németh ◽  
S. Csorba ◽  
C. Farkas
Keyword(s):  
Soil Moisture ◽  
Drought Stress ◽  
Water Balance ◽  
Water Content ◽  
Stress Tolerance ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Columns ◽  
Undisturbed Soil ◽  
Wheat Genotypes

Biotic and abiotic stress effects can limit the productivity of plants to great extent. In Hungary, drought is one of the most important constrains of biomass production, even at the present climatic conditions. The climate change scenarios, developed for the Carpathian basin for the nearest future predict further decrease in surface water resources. Consequently, it is essential to develop drought stress tolerant wheat genotypes to ensure sustainable and productive wheat production under changed climate conditions. The aim of the present study was to compare the stress tolerance of two winter wheat genotypes at two different scales. Soil water regime and development of plants, grown in a pot experiment and in large undisturbed soil columns were evaluated. The pot experiments were carried out in a climatic room in three replicates. GK Élet wheat genotype was planted in six, and Mv Emese in other six pots. Two pots were left without plant for evaporation studies. Based on the mass of the soil columns without plant the evaporation from the bare soil surface was calculated in order to distinguish the evaporation and the transpiration with appropriate precision. A complex stress diagnosis system was developed to monitor the water balance elements. ECH<sub>2</sub>O type capacitive soil moisture probes were installed in each of the pots to perform soil water content measurements four times a day. The irrigation demand was determined according to the hydrolimits, derived from soil hydrophysical properties. In case of both genotypes three plants were provided with the optimum water supply, while the other three ones were drought-stressed. In the undisturbed soil columns, the same wheat genotypes were sawn in one replicate. Similar watering strategy was applied. TDR soil moisture probes were installed in the soil at various depths to monitor changes in soil water content. In order to study the drought stress reaction of the wheat plants, microsensors of 1.6 mm diameter were implanted into the stems and connected to a quadrupole mass spectrometer for gas analysis. The stress status was indicated in the plants grown on partly non-irrigated soil columns by the lower CO<sub>2</sub> level at both genotypes. It was concluded that the developed stress diagnosis system could be used for soil water balance elements calculations. This enables more precise estimation of plant water consumption in order to evaluate the drought sensitivity of different wheat genotypes.

Soil Water Measurement as Basis to Optimize Irrigation Scheduling: A Review

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 553f-554
Author(s):  
A.K. Alva ◽  
A. Fares
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Real Time ◽  
Soil Water Content ◽  
Root Zone ◽  
Soil Water Potential ◽  
Irrigation Scheduling ◽  
Rooting Depth ◽  
The Impact

Supplemental irrigation is often necessary for high economic returns for most cropping conditions even in humid areas. As irrigation costs continue to increase more efforts should be exerted to minimize these costs. Real time estimation and/or measurement of available soil water content in the crop root zone is one of the several methods used to help growers in making the right decision regarding timing and quantity of irrigation. The gravimetric method of soil water content determination is laborious and doesn't suite for frequent sampling from the same location because it requires destructive soil sampling. Tensiometers, which measure soil water potential that can be converted into soil water content using soil moisture release curves, have been used for irrigation scheduling. However, in extreme sandy soils the working interval of tensiometer is reduced, hence it may be difficult to detect small changes in soil moisture content. Capacitance probes which operate on the principle of apparent dielectric constant of the soil-water-air mixture are extremely sensitive to small changes in the soil water content at short time intervals. These probes can be placed at various depths within and below the effective rooting depth for a real time monitoring of the water content. Based on this continuous monitoring of the soil water content, irrigation is scheduled to replenish the water deficit within the rooting depth while leaching below the root zone is minimized. These are important management practices aimed to increase irrigation efficiency, and nutrient uptake efficiency for optimal crop production, while minimizing the impact of agricultural non-point source pollutants on the groundwater quality.

scholarly journals Evaluation of Soil Water Content Measurements with Capacitance Probes to Support Irrigation Scheduling in a “Red Beaut” Japanese Plum Orchard

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1757
Author(s):  
Sandra Millán ◽  
Carlos Campillo ◽  
Antonio Vivas ◽  
María José Moñino ◽  
Maria Henar Prieto
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Successful Implementation ◽  
Stem Water Potential ◽  
Japanese Plum

Advances in electromagnetic sensor technologies in recent years have made automated irrigation scheduling a reality through the use of state-of-the-art soil moisture sensing devices. However, correct sensor positioning and interpretation of the measurements are key to the successful implementation of these management systems. The aim of this study is to establish guidelines for soil moisture sensor placement to support irrigation scheduling, taking into account the physiological response of the plant. The experimental work was carried out in Vegas Bajas del Guadiana (Extremadura, Spain) on a drip-irrigated experimental orchard of the early-maturing Japanese plum cultivar “Red Beaut”. Two irrigation treatments were established: control and drying. The control treatment was scheduled to cover crop water needs. In the drying treatment, the fruit trees were irrigated as in control, except in certain periods (preharvest and postharvest) in which irrigation was suspended (drying cycles). Over 3 years (2015–2017), a series of plant parameters were analyzed in relation to the measurements provided by a battery of frequency domain reflectometry probes installed in different positions with respect to tree and dripper: midday stem water potential (Ψstem), sap flow, leaf stomatal conductance, net leaf photosynthesis and daily fraction of intercepted photosynthetically active radiation. After making a comparison of these measurements as indicators of plant water status, Ψstem was found to be the physiological parameter that detected water stress earliest. The drying cycles were very useful to select the probe positions that provided the best information for irrigation management and to establish a threshold in the different phases of the crop below which detrimental effects could be caused to the crop. With respect to the probes located closest to the drippers, a drop in the relative soil water content (RSWC) below 0.2 would not be advisable for “non-stress” scheduling in the preharvest period. When no deficit irrigation strategies are applied in the postharvest period, the criteria are similar to those of preharvest. However, the probes located between the dripper at 0.15 and 0.30 m depth provide information on moderate water stress if the RSWC values falls below 0.2. The severe tree water stress was detected below 0.1 RSWC in probes located at 60 cm depth from this same position.

scholarly journals Spatial Variation of Soil Moisture and its Effect on Lint Yield in a Deficit Irrigated Cotton Field

2017 ◽  
Author(s):  
Yujin Wen
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Management Practice ◽  
Lint Yield ◽  
Cotton Field ◽  
Spatial Correlations ◽  
Regulated Deficit Irrigation ◽  
Contour Map

ABSTRACTAs the population increases in Southwest Texas in recent years, the urban water demand is drastically increasing. Regulated deficit irrigation (RDI) is expected to be one of the potential water management practice for saving water while maintaining crop yield. A field experiment was conducted at the AgriLIFE Research center in Uvalde in summer 2008 to examine the water saving potential. Seven irrigation schemes and four varieties were assigned to the experimental field to test their effects on lint yield. As the spatial correlation of the soil moisture/ soil water content were suspected, a spatial analysis on lint yield and soil water content was conducted. The analysis results showed that: 1) The soil water contents showed spatial correlations, while the lint yield did not. 2) The relationship between lint yield and soil water content can be described by linear model better than spatial autocorrelation model. 3) The variogram fit of the soil water content showed a completed curve; the contour map generated using ordinary kriging illustrated the irrigation schemes effect well, and gradient effect was suspected. The variogram of the lint yield could not be fitted by a completed curve, which indicated that the sample field was not large enough to determine the variance function. Further study is needed to determine the slope effect on soil water content, and to improve the contour map precision of the lint yield.

Laboratory calibration of different soil moisture sensors in various soil types

2020 ◽  
Author(s):  
Urša Pečan ◽  
Damijana Kastelec ◽  
Marina Pintar
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Bulk Density ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Bulk Density ◽  
Soil Samples ◽  
Soil Types ◽  
Soil Moisture Sensors ◽  
Dry Soil

&lt;p&gt;Measurements of soil water content are particularly useful for irrigation scheduling. In optimal conditions, field data are obtained through a dense grid of soil moisture sensors. Most of the currently used sensors for soil water content measurements, measure relative permittivity, a variable which is mostly dependant on water content in the soil. Spatial variability of soil characteristics, such as soil texture and mineralogy, organic matter content, dry soil bulk density and electric conductivity can also alter measurements with dielectric sensors. So the question arises, whether there is a need for a soil specific calibration of such sensors and is it dependant on the type of sensor? This study evaluated the performance of three soil water content sensors (SM150T, Delta-T Devices Ltd, UK; TRIME-Pico 32, IMKO micromodultechnik GmbH, DE; MVZ 100, Eltratec trade, production and services d.o.o., SI) in nine different soil types in laboratory conditions. Our calibration approach was based on calibration procedure developed for undisturbed soil samples (Holzman et al., 2017). Due to possible micro location variability of soil properties, we used disturbed and homogenized soil samples, which were packed to its original dry soil bulk density. We developed soil specific calibration functions for each sensor and soil type. They ranged from linear to 5&lt;sup&gt;th&lt;/sup&gt; order polynomial. We calculated relative and actual differences in sensor derived and gravimetrically determined volumetric soil water content, to evaluate the errors of soil water content measured by sensors which were not calibrated for soil specific characteristics. We observed differences in performance of different sensor types in various soil types. Our results showed measurements conducted with SM150T sensors were within the range of manufacturer specified measuring error in three soil types for which calibration is not necessary but still advisable for improving data quality. In all other cases, soil specific calibration is required to obtain relevant soil moisture data in the field.&lt;/p&gt;

scholarly journals Optimizing the Positioning of Soil Moisture Monitoring Sensors in Winter Wheat Fields

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1707
Author(s):  
Xiaojun Shen ◽  
Jing Liang ◽  
Ketema Zeleke ◽  
Yueping Liang ◽  
Guangshuai Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Profile ◽  
Root Distribution ◽  
Soil Surface ◽  
Soil Moisture Sensors ◽  
Total Average

Collecting accurate real-time soil moisture data in crop root zones is the foundation of automated precision irrigation systems. Soil moisture sensors (SMSs) have been used to monitor soil water content (SWC) in crop fields for a long time; however, there is no generally accepted guideline for determining optimal number and placement of soil moisture sensors in the soil profile. In order to study adequate positioning for the installation of soil moisture sensors in the soil profile, six years of field experiments were carried out in North China Plain (NCP). Soil water content was measured using the gravimetric method every 7 to 10 days during six growing seasons of winter wheat (Triticum aestivum L), and root distribution was measured using a soil core method during the key periods of winter wheat growth. The results from the experimental data analysis show that SWC at different depths had a high linear correlation. In addition, the values of correlation coefficients decreased with increasing soil depth; the coefficient of variation (CV) of SWC was higher in the surface layers than in the deeper layers (depths were 0–40 cm, 0–60 cm, and 0–100 cm during the early, middle, and last stages of winter wheat, respectively); wheat roots were mainly distributed in the surface layer. According to an analysis of CV for SWC and root distribution, the depths of planned wetted layers were determined to be 0–40 cm, 0–60 cm, and 0–100 cm during the sowing to reviving stages (the early stage of winter wheat), returning green and jointing stages (the middle stage of winter wheat), and heading to maturity stage (the last stage of winter wheat), respectively. The correlation and R-cluster analyses of SWC at different layers in the soil profile showed that SMSs should be installed 10 and 30 cm below the soil surface during the winter wheat growing season. The linear regression model can be built using SWC at depths of 10 and 30 cm to predict total average SWC in the soil profile. The results of validation showed that the developed model provided reliable estimates of total average SWC in the planned wetted layer. In brief, this study suggests that suitable positioning of soil moisture sensors is at depths of 10 and 30 cm below the soil surface.

scholarly journals Soil water content and water balance simulation of Caragana korshinskii Kom. in the semiarid Chinese Loess Plateau

2014 ◽  
Vol 62 (2) ◽  
pp. 89-96 ◽  
Author(s):  
Shengqi Jian ◽  
Chuanyan Zhao ◽  
Shumin Fang ◽  
Kai Yu
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Evaporation ◽  
Canopy Interception ◽  
Plant Transpiration ◽  
Water Balance Equation ◽  
Caragana Korshinskii

Abstract In this paper, to evaluate the hydrological effects of Caragana korshinskii Kom., measured data were combined with model-simulated data to assess the C. korshinskii soil water content based on water balance equation. With measured and simulated canopy interception, plant transpiration and soil evaporation, soil water content was modeled with the water balance equation. The monthly variations in the modeled soil water content by measured and simulated components (canopy interception, plant transpiration, soil evaporation) were then compared with in situ measured soil water content. Our results shows that the modeled monthly water loss (canopy interception + soil evaporation + plant transpiration) by measured and simulated components ranges from 43.78 mm to 113.95 mm and from 47.76 mm to 125.63 mm, respectively, while the monthly input of water (precipitation) ranges from 27.30 mm to 108.30 mm. The relative error between soil water content modeled by measured and simulated components was 6.41%. To sum up, the net change in soil water (ΔSW) is negative in every month of the growing season. The soil moisture is approaching to wilting coefficient at the end of the growth season, and the soil moisture recovered during the following season.

The performance of Metop Advanced SCATterometer soil moisture data as a complementary source for the estimation of crop-soil water balance in Central Europe

2018 ◽  
Vol 156 (5) ◽  
pp. 577-598 ◽  
Author(s):  
S. Thaler ◽  
J. Eitzinger ◽  
M. Trnka ◽  
M. Možný ◽  
S. Hahn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Status ◽  
Soil Water Balance ◽  
Balance Model ◽  
Spatial Average ◽  
Soil Wetness

AbstractSimulation of the water balance in cropping systems is an essential tool, not only to monitor water status and determine drought but also to find ways in which soil water and irrigation water can be used more efficiently. However, besides the requirement that models are physically correct, the spatial representativeness of input data and, in particular, accurate precipitation data remain a challenge. In recent years, satellite-based soil moisture products have become an important data source for soil wetness information at various spatial-temporal scales. Four different study areas in the Czech Republic and Austria were selected representing Central European soil and climatic conditions. The performance of soil water content outputs from two different crop-water balance models and the Metop Advanced SCATterometer (ASCAT) soil moisture product was tested with field measurements from 2007 to 2011. The model output for soil water content shows that the crop model Decision Support System for Agrotechnology Transfer performs well during dry periods (<30% plant available soil moisture (ASM), whereas the soil water-balance model SoilClim presents the best results in humid months (>60% ASM). Moreover, the model performance is best in the early growing season and decreases later in the season due to biases in simulated crop-related above-ground biomass compared with the relatively stable grass canopy of the measurement sites. The Metop ASCAT soil moisture product, which presents a spatial average of soil surface moisture, shows the best performance under medium soil wetness conditions (30–50% ASM), which is related to low variation in precipitation frequency and under conditions of low-surface biomass (early vegetation season).

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