The use of electromagnetic (EM) soil moisture probes is proliferating rapidly, in two broad domains: in field and laboratory research; and in strongly practical applications such as irrigation scheduling in farms or horticultural enterprises, and hydrological monitoring. Numerous commercial EM probes are available for measurement of volumetric water content (θv), spanning a range of measurement principles, and of probe dimensions and sensing volumes. However probe calibration (i.e. the relationship of actual θv to probe electrical output) can shift, often substantially, with variations in parameters such as soil texture, organic matter content, wetness range, electrical conductivity and temperature. Hence a single-valued, manufacturer-supplied calibration function is often inadequate, forcing the user to seek an application-specific calibration. The purpose of this paper is to describe systematic procedures which probe users can use to check or re-determine the calibration of their selected probe(s). Given the wide diversity of operating principles and designs of commercially-available EM probes, we illustrate these procedures with results from our own calibrations of five different short probes (length of 5 to 20 cm). Users are strongly recommended to undertake such calibration checks, which provide both a) pre-use experience, and b) more reliable in-use data. and Používanie elektromagnetických (EM) snímačov vlhkosti pôdy sa rýchlo rozširuje tak v terénnom výskume, ako aj v laboratóriu. Sú používané v praktických aplikáciách ako je riadenie závlah na farmách a záhradách, ako aj v hydrologickom monitoringu. Pre meranie vlhkosti pôdy (θv) sú dostupné početné typy komerčných EM snímačov, založených na viacerých princípoch merania a snímače majú rozdielnu veľkosť snímaných objemov pôdy. Kalibračné krivky takýchto snímačov (t.j. závislosti medzi reálnou vlhkosťou pôdy θv a elektrickým výstupom snímača) sa môžu posúvať - niekedy podstatne - a to v závislosti od rozdielnych parametrov pôdy, ako je jej textúra, obsah organických látok, rozsah vlhkostí, elektrická vodivosť a teplota. Z toho vyplýva, že jednoznačná kalibračná krivka, dodávaná výrobcom je často neadekvátna, čo núti užívateľa snímač kalibrovať v špecifických podmienkach. Cieľom tohto príspevku je opísať procedúry, ktoré môžu byť použité užívateľmi pri rekalibrácii vybraných typov snímačov. Berúc do úvahy širokú paletu princípov EM snímačov, ilustrujeme tieto procedúry výsledkami vlastných kalibračných testov na piatich typoch krátkych snímačov (dĺžka od 5 do 20 cm). Užívateľom odporúčame rekalibráciu komerčných snímačov, ktorými získajú predbežné skúsenosti a spoľahlivejšie výsledky pri meraní vlhkosti pôdy.
Agriculture faces several challenges to use the available resources in a more environmentally sustainable manner. One of the most significant is to develop sustainable water management. The modern Internet of Things (IoT) techniques with real-time data collection and visualisation can play an important role in monitoring the readily available moisture in the soil. An automated Arduino-based low-cost capacitive soil moisture sensor has been calibrated and developed for data acquisition. A sensor- and soil-specific calibration was performed for the soil moisture sensors (SKU:SEN0193 - DFROBOT, Shanghai, China). A Repeatability and Reproducibility study was conducted by range of mean methods on clay loam, sandy loam and silt loam soil textures. The calibration process was based on the data provided by the capacitive sensors and the continuously and parallelly measured soil moisture content by the thermo-gravimetric method. It can be stated that the response of the sensors to changes in soil moisture differs from each other, which was also greatly influenced by different soil textures. Therefore, the calibration according to soil texture was required to ensure adequate measurement accuracy. After the calibration, it was found that a polynomial calibration function (R2 ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures.
The study examines possible water savings by replacing alfalfa with winter wheat in the Fergana Valley, located upstream of the Syrdarya River in Central Asia. Agricultural reforms since the 1990s have promoted this change in cropping patterns in the Central Asian states to enhance food security and social benefits. The water use of alfalfa, winter wheat/fallow, and winter wheat/green gram (double cropping) systems is compared for high-deficit, low-deficit, and full irrigation scenarios using hydrological modeling with the HYDRUS-1D software package. Modeling results indicate that replacing alfalfa with winter wheat in the Fergana Valley released significant water resources, mainly by reducing productive crop transpiration when abandoning alfalfa in favor of alternative cropping systems. However, the winter wheat/fallow cropping system caused high evaporation losses from fallow land after harvesting of winter wheat. Double cropping (i.e., the cultivation of green gram as a short duration summer crop after winter wheat harvesting) reduced evaporation losses, enhanced crop output and hence food security, while generating water savings that make more water available for other productive uses. Beyond water savings, this paper also discusses the economic and social gains that double cropping produces for the public within a broader developmental context.
During the last decade, biochar has captured the attention of agriculturalists worldwide due to its positive effect on the environment. To verify the biochar effects on organic carbon content, soil sorption, and soil physical properties under the mild climate of Central Europe, we established a field experiment. This was carried out on a silty loam Haplic Luvisol at the Malanta experimental site of the Slovak Agricultural University in Nitra with five treatments: Control (biochar 0 t ha–1, nitrogen 0 kg ha–1); B10 (biochar 10 t ha–1, nitrogen 0 kg ha–1); B20 (biochar 20 t ha–1, nitrogen 0 kg ha–1); B10+N (biochar 10 t ha–1, nitrogen 160 kg ha–1) and B20+N (biochar 20 t ha–1, nitrogen 160 kg ha–1). Applied biochar increased total and available soil water content in all fertilized treatments. Based on the results from the spring soil sampling (porosity and water retention curves), we found a statistically significant increase in the soil water content for all fertilized treatments. Furthermore, biochar (with or without N fertilization) significantly decreased hydrolytic acidity and increased total organic carbon. After biochar amendment, the soil sorption complex became fully saturated mainly by the basic cations. Statistically significant linear relationships were observed between the porosity and (A) sum of base cations, (B) cation exchange capacity, (C) base saturation.
The knowledge of snowpack distribution at a catchment scale is important to predict the snowmelt runoff. The objective of this study is to select and quantify the most important factors governing the snowpack distribution, with special interest in the role of different canopy structure. We applied a simple distributed sampling design with measurement of snow depth and snow water equivalent (SWE) at a catchment scale. We selected eleven predictors related to character of specific localities (such as elevation, slope orientation and leaf area index) and to winter meteorological conditions (such as irradiance, sum of positive air temperature and sum of new snow depth). The forest canopy structure was described using parameters calculated from hemispherical photographs. A degree-day approach was used to calculate melt factors. Principal component analysis, cluster analysis and Spearman rank correlation were applied to reduce the number of predictors and to analyze measured data. The SWE in forest sites was by 40% lower than in open areas, but this value depended on the canopy structure. The snow ablation in large openings was on average almost two times faster compared to forest sites. The snow ablation in the forest was by 18% faster after forest defoliation (due to the bark beetle). The results from multivariate analyses showed that the leaf area index was a better predictor to explain the SWE distribution during accumulation period, while irradiance was better predictor during snowmelt period. Despite some uncertainty, parameters derived from hemispherical photographs may replace measured incoming solar radiation if this meteorological variable is not available.