Data from: Estimating field capacity from volumetric soil water content time series using automated processing algorithms

Vadose zone measurements of volumetric soil water content (θ) using soil moisture sensors (SMSs) have become more common due to advances in technology and reduction of costs. Soil moisture sensor data exhibit a characteristic cyclical pattern reflecting water flux dynamics into and out of the observed soil volume. Expert review of SMS datasets to distinguish valid from corrupt or incomplete soil water cycles is arguably the most precise method for determining field capacity (θFC) but is impractically cumbersome and time consuming for increasingly large SMS datasets. We evaluated competing approaches for automated soil water cycles analysis that use widely available R packages based on pattern recognition and machine learning (findpeaks [R-FP], symbolic aggregate approximation [R-SAX], and density histogram [R-DH]), and a MATLAB code based on soil water dynamic principles (SWDP). These approaches were applied to three SMS datasets. Our empirical results showed superiority of R-SAX for identifying valid soil water cycles, probably due to benefiting from training sets to calibrate to correct cycles. Two other approaches (SWDP and R-FP) provided similar results without need of training sets or preprocessing data. Three approaches for estimating field capacity were applied to valid cycles, R-FP, regression of exponential decay (SWDP-R), and estimated “knee” of curve (SWDP-K). Each performed similarly to the expert defined values, with R-FP and SWDP-R generally performing best across analyses. Results of this study also show temporal dynamics of θFC within datasets used here. There is potential for optimizing θFC and a need for automated, objective analysis to leverage dynamics in irrigation management and modeling

Plant response to evapotranspiration and soil water sensor irrigation scheduling methods for papaya production in south Florida

An irrigation study was conducted to determine the effects of implementing different irrigation practices on growth and yields of papaya plants in south Florida. Treatments included using automated switching tensiometers based on soil water status, irrigation based on ET calculated from historic weather data and a set schedule irrigation regime. The study consisted of two trials (2006-2007 and 2008-2009). Water volumes applied, plant height and diameter, leaf gas exchange, leaf petiole nutrient levels, fruit yields and fruit total soluble solids were measured throughout the study. For both trials, significantly more water was applied in the set schedule irrigation treatment than in all other treatments; historic ET and soil water based treatments received only about 31-36% of the water applied in the set schedule irrigation. Trunk diameter and plant height per unit water volume applied values for the set schedule treatment were significantly lower than those from all other treatments during both trials. The set schedule treatment in both trials also had the lowest crop production water use efficiency (CP-WUE); CP-WUE values among all other treatments were generally not significantly different from each other. Soil water and historic ET-based irrigation methods were identified as more sustainable practices compared to set schedule irrigation due to the lower water volumes applied while maintaining plant nutrient content, growth, photosynthetic rates, and fruit yields for this production system.Irrigation scheduling Papaya Evapotranspiration Soil water potential Plant water stress Petiole nutrient content Carica papaya

A Smart Irrigation Tool to Determine the Effects of ENSO on Water Requirements for Tomato Production in Mozambique

Irrigation scheduling is used by growers to determine the right amount and timing of water application. In most parts of Mozambique, 90% of the total yearly precipitation occurs from November to March. The El Niño Southern Oscillation (ENSO) phenomenon influences the climate in Mozambique and affects the water demand for crop production. The objectives of this work were to quantify the effects of ENSO phenomenon on tomato crop water requirements, and to create the AgroClimate irrigation tool (http://mz.agroclimate.org/) to assist farmers in improving irrigation management. This study was based on daily grid-based climate information from 1983 to 2016 from the Climate Forecast System Reanalysis. Daily crop evapotranspiration was calculated by Hargreaves equation and crop coefficients. This tool is available online and considers different planting dates, ENSO phases, and crop growing season lengths. Irrigation needs varied from less than 250 mm per growing cycle during winter to 550 mm during spring. Both El Niño and La Niña influenced the irrigation scheduling, especially from November to March. El Niño periods were related to increased water demand due to drier and warmer conditions, while the opposite was observed for La Niña. The ENSO information might be used to understand climate variability and improve tomato irrigation scheduling in Mozambique

Soil Water Dynamics of Shallow Water Table Soils Cultivated With Potato Crop

Agricultural areas with shallow water tables usually rely on upward soil water flux to supply crop evapotranspiration (ETc). The study objective was to determine optimum water table levels for coarse-textured soils cultivated with potato ( L.) by estimating the upward soil water flux under different irrigation methods. Potato was grown under seepage, subirrigation with tile drainage, subsurface drip irrigation (SDI), and sprinkler irrigation. Irrigation zones were classified as sandy soil with low soil organic matter (SOM) and high bulk density (), or loamy sand soil with high SOM and low . Upward soil water flux supplied enough water to the root zone to meet ETc when the water table was at the 69-cm depth for loamy sand soils under seepage, and 42 and 45 cm for sandy soils under subirrigation and SDI, respectively. The sprinkler-irrigated area had no control over the water table, whereby the cumulative contribution of upward water flux still averaged 6.3 cm, suggesting that irrigation rates could be reduced if the water table is controlled and upward flux accounted for in the crop water balance. Rainfall introduces flooding risks and crop losses, but these risks are minimized with management. The water table elevation/precipitation ratio was 34.4 and 25.6 cm cm of rain for loamy sand and sandy soils. After precipitation, the water table returned to the original levels twice as fast under subirrigation than with other methods given improved drainage capacity. Soil characteristics, irrigation method, upward water flux, and proper water table management are important factors for maintaining ideal soil moisture conditions in the crop root zone, minimizing flooding risk