Predicting cadmium fractions in agricultural soils using proximal sensing techniques

Cadmium (Cd) accumulation in agricultural systems has caused global environmental and health concerns. Application of phosphate fertiliser to sustain plant production unintentionally accumulated Cd in agricultural soils over time. Rapid and cost-effective Cd monitoring in these soils will help to inform Cd management practices. Compared to total Cd analysis, examining chemical fractions by sequential extraction methods can provide information on the origin, availability, and mobility of soil Cd, and to assess the potential plant Cd uptake. A total of 87 air-dried topsoil (0-15 cm) samples from pastoral farms with a history of long-term application of phosphate fertiliser were analysed using wet chemistry methods for total Cd and Cd forms in exchangeable, acid soluble, metal oxides bound, organic matter bound, and residual fractions. The data acquired using three proximal sensing techniques, visible-near-infrared (vis-NIR), mid-infrared (MIR), and portable X-ray fluorescence (pXRF) spectroscopy were used as input for partial least squares regression to develop models predicting total Cd and Cd fractions. The average total Cd concentration was 0.58 mg Cd/kg soil. For total Cd, cross-validation (cv) results of models using individual vis-NIR, MIR, and pXRF data performed with normalised root mean squared error (nRMSEcv) of 26%, 30%, and 31% and concordance correlation coefficient (CCCcv) of 0.85, 0.77, and 0.75, respectively. For exchangeable Cd, model using MIR data performed with nRMSEcv of 40% and CCCcv of 0.57. For acid soluble and organic matter bound Cd, models using vis-NIR data performed with nRMSEcv of 11% and 33% and CCCcv of 0.97 and 0.84, respectively. Reflectance spectroscopy techniques could potentially be applied as complementary tools to estimate total Cd and plant available and potentially available Cd fractions for effective implementation of Cd monitoring programmes.

Clean and accurate soil quality monitoring in mining areas under environmental rehabilitation in the Eastern Brazilian Amazon

Soil quality monitoring in mining rehabilitation areas is a crucial step to validate the effectiveness of the adopted recovery strategy, especially in critical areas for environmental conservation, such as the Brazilian Amazon. The use of portable X-ray fluorescence (pXRF) spectrometry allows a rapid quantification of several soil chemical elements, with low cost and without residue generation, being an alternative for clean and accurate environmental monitoring. Thus, this work aimed to assess soil quality in mining areas with different stages of environmental rehabilitation based on predictions of soil fertility properties through pXRF along with four machine learning algorithms (projection pursuit regression, PPR; support vector machine, SVM; cubist regression, CR; and random forest, RF) in the Eastern Brazilian Amazon. Sandstone and iron mines in different chronological stages of rehabilitation (initial, intermediate, and advanced) were evaluated, in addition to non-rehabilitated and native forest areas. A total of 81 soil samples (26 from sandstone mine and 55 from iron mine) were analyzed by both traditional wet-chemistry methods and pXRF. The available/exchangeable contents of K, Ca, B, Fe, and Al, in addition to H+Al, cation exchange capacity at pH = 7, Al saturation, soil organic matter, pH, sum of bases, base saturation, clay, and sand were accurately predicted (R2 > 0.70) using pXRF data, with emphasis on the prediction of Fe (R2 = 0.93), clay content (R2 = 0.81), H+Al (R2 = 0.81), and K+ (R2 = 0.85). The best predictive models were developed by RF and CR (86%) and when considering pXRF data + mining area + stage of rehabilitation (73%). The results highlight the potential of pXRF to accurately assess soil properties in environmental rehabilitation areas in the Amazon region (yet scarcely evaluated under this approach), promoting a more agile and cheaper preliminary diagnosis compared to traditional methods.

Hyperspectral Proximal Sensing for Estimating Photosynthetic Capacities at Leaf and Canopy Scales

Agronomists, plant breeders, and plant biologists have been promoting the need to develop high-throughput methods to measure plant traits of interest for decades. Measuring these plant traits or phenotypes is often a bottleneck since skilled personnel, resources, and ample time are required. Additionally, plant phenotypic traits from only a select number of breeding lines or varieties can be quantified because the "gold standard" measurement of a desired trait cannot be completed in a timely manner. As such, numerous approaches have been developed and implemented to better understand the biology and production of crops and ecosystems. In this chapter, we explain one of the recent approaches leveraging hyperspectral measurements to estimate different aspects of photosynthesis. Notably, we outline the use of hyperspectral radiometer and imaging to rapidly estimate two of the rate-limiting steps of photosynthesis: the maximum rate of the carboxylation of Rubisco (Vcmax) and the maximum rate of electron transfer or regeneration of RuBP (Jmax).

"Estimating soil surface roughness by proximal sensing for soil erosion modeling implementation at field scale"

Soil Surface Roughness (SSR) is a physical feature of soil microtopography, which is strongly influenced by tillage practices and plays a key role in hydrological and soil erosion processes. Therefore, surface roughness indices are required when using models to estimate soil erosion rates, where tabular values or direct measurements can be used. Field measurements often imply out-of-date and time-consuming methods, such as the pin meter and the roller chain, providing inaccurate indices. A novel technique for SSR measurement has been adopted, employing an RGB-Depth camera to produce a small-scale Digital Elevation Model of the soil surface, in order to extrapolate roughness indices. Canopy cover coverage (CC) of the cover crop was also detected from the camera's images. The values obtained for SSR and CC indices were implemented in the MMF (Morgan-Morgan-Finney) model, to validate the reliability of the proposed methodology by comparing the models' results for sediment yields with long-term soil erosion measurements in sloping vineyards in NW Italy. The performance of the model in predicting soil losses was satisfactory to good for a vineyard plot with inter-rows managed with recurrent tillage, and it was improved using spatialized soil roughness input data with respect to a uniform value. Performance for plot with permanent ground cover was not so good, however it was also improved using spatialized data. The measured values were also useful to obtain C-factor for RUSLE application, to be used instead of tabular values.

An expertized grapevine disease image database including five grape varieties focused on Flavescence dorée and its confounding diseases, biotic and abiotic stresses

The grapevine is vulnerable to diseases, deficiencies, and pests, leading to significant yield losses. Current disease controls involve monitoring and spraying phytosanitary products at the vineyard block scale. However, automatic detection of disease symptoms could reduce the use of these products and treat diseases before they spread. Flavescence dorée (FD), a highly infectious disease that causes significant yield losses, is only diagnosed by identifying symptoms on three grapevine organs: leaf, shoot, and bunch. Its diagnosis is carried out by scouting experts, as many other diseases and stresses, either biotic or abiotic, imply similar symptoms (but not all at the same time). These experts need a decision support tool to improve their scouting efficiency. To address this, a dataset of 1483 RGB images of grapevines affected by various diseases and stresses, including FD, was acquired by proximal sensing. The images were taken in the field at a distance of 1-2 meters to capture entire grapevines and an industrial flash was ensuring a constant luminance on the images regardless of the environmental circumstances. Images of 5 grape varieties (Cabernet sauvignon, Cabernet franc, Merlot, Ugni blanc and Sauvignon blanc) were acquired during 2 years (2020 and 2021). Two types of annotations were made: expert diagnosis at the grapevine scale in the field and symptom annotations at the leaf, shoot, and bunch levels on computer. On 744 images, the leaves were annotated and divided into three classes: 'FD symptomatic leaves', 'Esca symptomatic leaves', and 'Confounding leaves'. Symptomatic bunches and shoots were, in addition of leaves, annotated on 110 images using bounding boxes and broken lines, respectively. Additionally, 128 segmentation masks were created to allow the detection of the symptomatic shoots and bunches by segmentation algorithms and compare the results to those of the detection algorithms.

Proximal hyperspectral sensing of abiotic stresses in plants

Recent attempts, advances and challenges, as well as future perspectives regarding the application of proximal hyperspectral sensing (where sensors are placed within 10 m above plants, either on land-based platforms or in controlled environments) to assess plant abiotic stresses have been critically reviewed. Abiotic stresses, caused by either physical or chemical reasons such as nutrient deficiency, drought, salinity, heavy metals, herbicides, extreme temperatures, and so on, may be more damaging than biotic stresses (affected by infectious agents such as bacteria, fungi, insects, etc.) on crop yields. The proximal hyperspectral sensing provides images at a sub-millimeter spatial resolution for doing an in-depth study of plant physiology and thus offers a global view of the plant's status and allows for monitoring spatio-temporal variations from large geographical areas reliably and economically. The literature update has been based on 362 research papers in this field, published from 2010, most of which are from four years ago and, in our knowledge, it is the first paper that provides a comprehensive review of the applications of the technique for the detection of various types of abiotic stresses in plants.

Peatlands spectral data influence in global spectral modelling of soil organic carbon and total nitrogen using visible-near-infrared spectroscopy

Peatlands ecosystem is one of the largest global terrestrial carbon pools. However, there is a shortness of its characterisation and information through new proximal sensing approaches. The visible and near-infrared spectroscopy is an inexpensive, quick, non-evasive, proximal sensing and low-cost analysis employed in field and/or laboratory. Despite that, there is another current issue in using this tool for creating global models, which is how it can retrieve local characteristics such as soil organic carbon (SOC) and total nitrogen (TN) in peatlands ecosystems. The aims in this study were to: (i) create a local model for quantifying SOC and TN finding the best pre-processing and machine learning methods in peatlands ecosystem, and (ii) evaluate the contribution of SOC and TN data collected in that ecosystem to global models in European Union. The hypothesis was that the SOC and TN data sampled in peatlands ecosystem can improve analytical quantification of those soil properties. The soil and spectral datasets were retrieved from the Land Use/Cover Area frame Statistical Survey with 21,771 observations at 0-20 cm depth and 63 soil cores in a degraded peatland in Germany with 262 observations up to 2 m depth. We evaluated three spectral pre-processing techniques with the Partial Least Square Regression (PLSR), Random Forest (RF), and Cubist machine learning algorithms. The best pre-processing technique was achieved applying Savitzky-Golay smoothing with a window size of 71 points, 2nd order polynomial, and zero derivative with Cubist algorithm for both SOC and TN predictions. Furthermore, merging the local with global data for global modelling demonstrated to improve SOC and TN predictions because of the local data representativeness and quality. Therefore, the SOC and TN data sampled in peatlands ecosystem can improve quantification of those soil properties in field and laboratory, which are crucial proxies for GHG emissions and climate change.

Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects

BACKGROUND

Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge.

METHODS

As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)].

RESULTS

For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2).

CONCLUSION

We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.

4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography

BACKGROUND

Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA).

RESULTS

Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R²) of 0.84 and a P < 0.001.

CONCLUSIONS

The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.

Leaf water potential of field crops estimated using NDVI in ground-based remote sensing-opportunities to increase prediction precision

Remote-sensing using normalized difference vegetation index (NDVI) has the potential of rapidly detecting the effect of water stress on field crops. However, this detection has typically been accomplished only after the stress effect led to significant changes in crop green biomass, leaf area index, angle and position, and few studies have attempted to estimate the uncertainties of the regression models. These have limited the informed interpretation of NDVI data in agricultural applications. We built a ground-based sensing cart and used it to calibrate the relationships between NDVI and leaf water potential (LWP) for wheat, corn, and cotton growing under field conditions. Both the methods of ordinary least-squares (OLS) and weighted least-squares (WLS) were employed in data analysis, and measurement errors in both LWP and NDVI were considered. We also used statistical resampling to test the effect of measurement errors of LWP on the uncertainties of model coefficients. Our data showed that obtaining a high value of the coefficient of determination did not guarantee a high prediction precision in the obtained regression models. Large prediction uncertainties were estimated for all three crops, and the regressions obtained were not always significant. The best models were obtained for cotton with a prediction uncertainty of 27%. We found that considering measurement errors for both LWP and NDVI led to reduced uncertainties in model coefficients. Also, reducing the sample size of LWP measurement led to significantly increased uncertainties in the coefficients of the linear models describing the LWP-NDVI relationship. Finally, potential strategies for reducing the uncertainty relative to the range of NDVI measurement are discussed.

An annotated image dataset of downy mildew symptoms on Merlot grape variety

This article introduces a dataset of high-resolution colour images of grapevines. It contains 99 images acquired in the vineyard from a cruising tractor. Each image includes the full foliage of a grapevine plant. These images display a diverse range of symptoms caused by the grapevine downy mildew (Plasmopara viticola), a major fungal disease. The dataset also includes various confounding factors, i.e. anomalies that are not related to the disease.  These anomalies are the natural and common phenomena affecting vineyards such as results of mechanical wounds, necroses, chemical burns or yellowing and discolorations due to nutritional or hydric deficiencies. Images were acquired in-situ on "Le Domaine de la Grande Ferrade" a public experimental facility of INRAE, in the area of Bordeaux. Acquisitions took place at early fruiting stages (BBCH 75-79) corresponding to the main sanitary pressure during growth. The acquisition device, embedded on a vine tractor, is composed of an industrial colour camera synchronised with powerful flashes. The purpose of this device is to produce a "day for night" effect that mitigates the variation of sunlight. It enables to homogenise images acquired during different weathers and time of the day and to ensure that the foreground (containing foliage) displays appropriate brightness, with minimum shadows while the background is darker. The images of the dataset were annotated manually by photo-interpretation with a careful review of expertise regarding phytopathology and physiological disorders. The annotation process consists in associating pixels with a class that defines its membership to a type of organ and its physiological state. Pixels from healthy, symptomatic or abnormal grapevine tissues were labelled into seven classes: "Limbus", "Leaf edges", "Berries", "Stems", "Foliar mildew", "Berries mildew" and "Anomalies". The annotation is achieved with the GIMP2 software as mask images where the value attributed to each pixel corresponds to one of the seven considered classes.

Rapid and cost-effective nutrient content analysis of cotton leaves using near-infrared spectroscopy (NIRS)

The development of portable near-infrared spectroscopy (NIRS) combined with smartphone cloud-based chemometrics has increased the power of these devices to provide real-time in-situ crop nutrient analysis. This capability provides the opportunity to address nutrient deficiencies early to optimise yield. The agriculture sector currently relies on results delivered via laboratory analysis. This involves the collection and preparation of leaf or soil samples during the growing season that are time-consuming and costly. This delays farmers from addressing deficiencies by several weeks which impacts yield potential; hence, requires a faster solution. This study evaluated the feasibility of using NIRS in estimating different macro- and micronutrients in cotton leaf tissues, assessing the accuracy of a portable handheld NIR spectrometer (wavelength range of 1,350–2,500 nm). This study first evaluated the ability of NIRS to predict leaf nutrient levels using dried and ground cotton leaf samples. The results showed the high accuracy of NIRS in predicting essential macronutrients (0.76 ≤ R² ≤ 0.98 for N, P, K, Ca, Mg and S) and most micronutrients (0.64 ≤ R² ≤ 0.81 for Fe, Mn, Cu, Mo, B, Cl and Na). The results showed that the handheld NIR spectrometer is a practical option to accurately measure leaf nutrient concentrations. This research then assessed the possibility of applying NIRS on fresh leaves for potential in-field applications. NIRS was more accurate in estimating cotton leaf nutrients when applied on dried and ground leaf samples. However, the application of NIRS on fresh leaves was still quite accurate. Using fresh leaves, the prediction accuracy was reduced by 19% for macronutrients and 11% for micronutrients, compared to dried and ground samples. This study provides further evidence on the efficacy of using NIRS for field estimations of cotton nutrients in combination with a nutrient decision support tool, with an accuracy of 87.3% for macronutrients and 86.6% for micronutrients. This application would allow farmers to manage nutrients proactively to avoid yield penalties or environmental impacts.

Assessing soil contamination in automobile scrap yards by portable X-ray fluorescence spectrometry and magnetic susceptibility

A by-product of industrialization and population growth, automobile scrap yards are a potential source of metal contamination in soil. This study evaluated the use of portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (χ) analysis in assessing metal soil contamination in scrap yards located in Brazil. Five automobile scrap yards were selected in Curitiba, Paraná State (CB1, CB2, and CB3) and Lavras, Minas Gerais State (LV1 and LV2). By evaluating metal concentrations and geoaccumulation index values, we verified moderate Cu, Pb, and Zr contamination and moderate to high Zn contamination, primarily in the topsoil (0-10 cm). Soil Zn concentrations in automobile scrap yards were on average four times higher than in reference soils, suggesting that galvanized automobile parts may be the primary source of this soil contaminant. Although other elements (i.e., As, Cr, Fe, Nb, Ni, and Y) were slightly increased compared to reference values in one or more soils, concentrations did not constitute contamination. Automobile scrap yard topsoil had higher χ values (5.8 to 52.9 × 10^-7 m³ kg^-1) at low frequency (χ_lf) compared to reference soil (3.6 to 7.5 × 10^-7 m³ kg^-1). The highest values of χ_lf occurred in LV soils, which also represented the highest Zn contamination. Magnetic multidomain characteristics (percent frequency-dependent susceptibility between 2 and 10) indicated magnetic particle contributions of anthropogenic origin. The use of pXRF and χ_lf as non-destructive techniques displays potential for identifying soil contamination in automobile scrap yards.

Aerial application methods control spider mites on corn in Kansas, USA

The Banks grass mite, Oligonychus pratensis (Banks), and two-spotted spider mite, Tetranychus urticae Koch, are important chelicerae herbivores on irrigated corn in Kansas, USA. They cause loss of foliage, stalk breakage, kernel shrinkage and yield loss. Aerial application methods were evaluated to control spider mites in a commercial corn field in August, 2017, near Hoxie, Kansas. Dimethoate (0.56 kg active ingredient/ha) and Lorsban Advanced (1.05 kg active ingredient/ha) mixed with a nonionic surfactant, Traverse (0.25% v/v), were aerially applied using conventional flat-fan hydraulic nozzles at 28.1 L/ha and aerial electrostatic nozzles at 9.3 L/ha. To assess spray droplet spectra of the aerial application methods, water sensitive paper samplers were deployed at the whorl of husk leaves at the tip of the ear before aerial spray treatments were applied. Spray droplet spectra were quantified using commercial image analysis software. Treatment efficacy was assessed both objectively and subjectively. Objective efficacy evaluation incorporated the use of an active multispectral optical sensor via spectral analysis of the midrib regions of corn leaves on the abaxial surface where spider mites reside. Subjective damage ratings based upon in-field spider mite movement observations were scored by professional crop scouting consultants. Results of objective spectral analysis and subjective damage ratings indicated that both the conventional and electrostatic nozzles with 283 and 210-µm spray droplet 'volume median diameter' (VMD) at 28.1 and 9.3 L/ha, respectively, controlled spider mites compared to an untreated check.

Are optical indices good proxies of seasonal changes in carbon fluxes and stress-related physiological status in a beech forest?

This study investigates the functionality of a Mediterranean-mountain beech forest in Central Italy using simultaneous determinations of optical measurements, carbon (C) fluxes, leaf eco-physiological and biochemical traits during two growing seasons (2014-2015). Meteorological variables showed significant differences between the two growing seasons, highlighting a heat stress coupled with a reduced water availability in mid-summer 2015. As a result, a different C sink capacity of the forest was observed between the two years of study, due to the differences in stressful conditions and the related plant physiological status. Spectral indices related to vegetation (VIs, classified in structural, chlorophyll and carotenoid indices) were computed at top canopy level and used to track CO₂ fluxes and physiological changes. Optical indices related to structure (EVI 2, RDVI, DVI and MCARI 1) were found to better track Net Ecosystem Exchange (NEE) variations for 2014, while indices related to chlorophylls (SR red edge, CL red edge, MTCI and DR) provided better results for 2015. This suggests that when environmental conditions are not limiting for forest sink capacity, structural parameters are more strictly connected to C uptake, while under stress conditions indices related to functional features (e.g., chlorophyll content) become more relevant. Chlorophyll indices calculated with red edge bands (SR red edge, NDVI red edge, DR, CL red edge) resulted to be highly correlated with leaf nitrogen content (R²>0.70), while weaker, although significant, correlations were found with chlorophyll content. Carotenoid indices (PRI and PSRI) were strongly correlated with both chlorophylls and carotenoids content, suggesting that these indices are good proxies of the shifting pigment composition related to changes in soil moisture, heat stress and senescence. Our work suggests the importance of integrating different methods as a successful approach to understand how changing climatic conditions in the Mediterranean mountain region will impact on forest conditions and functionality.

Irrigation salinity hazard assessment and risk mapping in the lower Macintyre Valley, Australia

In the Murray-Darling Basin of Australia, secondary soil salinization occurs due to excessive deep drainage and the presence of shallow saline water tables. In order to understand the cause and best management, soil and vadose zone information is necessary. This type of information has been generated in the Toobeah district but owing to the state border an inconsistent methodology was used. This has led to much confusion from stakeholders who are unable to understand the ambiguity of the results in terms of final overall risk of salinization. In this research, a digital soil mapping method that employs various ancillary data is presented. Firstly, an electromagnetic induction survey using a Geonics EM34 and EM38 was used to characterise soil and vadose zone stratigraphy. From the apparent electrical conductivity (ECa) collected, soil sampling locations were selected and with laboratory analysis carried out to determine average (2-12m) clay and EC of a saturated soil-paste extract (ECe). EM34 ECa, land surface parameters derived from a digital elevation model and measured soil data were used to establish multiple linear regression models, which allowed for mapping of various hazard factors, including clay and ECe. EM38 ECa data were calibrated to deep drainage obtained from Salt and Leaching Fraction (SaLF) modelling of soil data. Expert knowledge and indicator kriging were used to determine critical values where the salinity hazard factors were likely to contribute to a shallow saline water table (i.e., clay ≤35%; ECe>2.5dS/m, and deep drainage >100mm/year). This information was combined to produce an overall salinity risk map for the Toobeah district using indicator kriging. The risk map shows potential salinization areas and where detailed information is required and where targeted research can be conducted to monitor soil conditions and water table heights and determine best management strategies.