Water table control has been successfully tested to improve the sustainability of water management in cranberry production. In the province of Québec (Canada), three sites were investigated to determine the optimum water table depth below soil surface (WTD) using three criteria: (1) increasing yield without decreasing fruit quality; (2) minimizing the amount of water needed by the sprinkler system; and (3) avoiding hypoxic stresses in the rhizosphere. Our results show that the final yield, the berry sugar content, the total number of berries, the number of berries per upright, and the fruit set were maximized when the WTD was 60 cm. Sprinkler water savings of 77% were obtained where the WTD was shallower than 66 cm. In order to avoid hypoxic conditions due to poor drainage, the water level in the canals surrounding the beds should be lowered to 80 cm when a rainfall or a frost protection irrigation is anticipated. All sides of a block of beds must be surrounded by canals to ensure a uniform WTD and to avoid lateral hydraulic gradients that could cause peripheral seepage losses.

Several studies have pointed out the promising use of nutritional diagnosis methods for the determination of optimum nutrient contents in plant tissues. The present investigation was carried out in different oases in Southern Tunisia to determine reference values for the interpretation of leaf analyses of date palm (Phoenix dactylifera) Deglet Nour cultivar with the Critical Value Approach (CVA) and the Compositional Nutrient Diagnosis (CND). A database (n = 100) of yield and mineral concentrations taken from date palm leaflets in October, at the maturity stage of dates, was used. The yield cut-off between low-yield and high-yield subpopulations, selected from cumulative variance ratio functions across survey data, was 76 kg palm⁻¹ and the global nutrient imbalance index (CNDr²) was 10.06. Critical CND nutrient indices were found to be symmetrical around zero as follows: (1.59; +1.59) for I_N, (−0.44, +0.44) for I_P, (−0.63, +0.63) for I_K, (−0.94, +0.94) for I_Ca, (−1.05, +1.05) for I_Mg, (−0.80, +0.80) for I_Fe, (−0.74, +0.74) for I_Cu, (−0.80, +0.80) for I_B, (−0.93, +0.93) for I_Zn, (−1.04, +1.04) for I_Mn, and (−1.03, +1.03) for the residual value. Compared to CND, the CVA approach shows weak detection of the nutrients that cause nutritional imbalance. CND indices revealed, except for N, the presence of nutrient imbalances and the necessity to correct the mineral nutrition of date palm in the Kebeli oases.

In this paper we first compare three different methods of spatial interpolation, i.e., inverse distance weighting (IDW), thin plate splines (TPS), and kriging on weekly water table depth (WTD) measurements from 80 observation wells in two cranberry farms (Farm A and Farm B) located in Québec, Canada. We use the leave-one-out cross-validation approach to assess the performance of the methods. Second, we evaluate the influence of the density of measurement points over the interpolation error for the cited methods. Third, we assess the performance of drainage systems and their impacts on crop productivity as a result of cumulative rainfall. Results along with practical considerations show that TPS is the best interpolator for WTD and this superiority is maintained and further demonstrated through a sensitivity analysis of the methods to spatial sampling density, i.e., partitioning the data into subsets of 25, 50, and 75% of the dataset. However, the random approach for selecting these subsets shows an unexpected result; that is, the interpolation methods exhibit a higher performance in terms of the Pearson correlation (r) for the 25% data subset at Farm B. Meanwhile, the cumulative precipitation over a three-day period, the maximum time required to return the soil matric potential to the optimal value after a major rainfall event, had a steady influence on WTD and thus crop productivity in the studied farms. This influence is more apparent for Farm A, but a rather random effect is noted for Farm B. This study presents a water-management-based strategy that mitigates the supplementary cost and effort for sensor deployment in water table monitoring for cranberry production. It is therefore of practical interest to cranberry growers and decision-makers who aim to maximize yields through water-management-oriented strategies.

Despite its inconveniences, the most recognized method to extract boron from soils is that of hot water extraction (B_HW), which is used for diagnostics and recommendations. However, the Mehlich-3 (M3) method is widely used to extract and diagnose several elements at once (P, K, Ca, Mg, Al, B, Cu, Zn, Fe, and Mn) and is well adapted to routine analyses. The objective of our study was to develop a soil diagnostic and recommendation system for boron as a function of measured B_M3 (and other interacting elements), crop type, and spreading methods. This system is based on three databases from either the international literature or the chemical characterization of acidic-to-neutral soils typical from Québec (Canada). The first database came from the characterization of 365 samples typical of Québec soils; it has been used to predict, by the AutoML (Automatic Machine Learnig) supervised learning algorithm, B_M3 as a function of a set of parameters from the following: B_HW, pH_W, organic carbon (OC), Ca_M3, K_M3, and Mg_M3. Depending on the parameters used, the R² between the measured and observed B_M3 varied from 0.36 to 0.99. This database allowed us to define two classifications for soil boron diagnostics and fertility evaluation. The Cate–Nelson analysis for these two models allowed us to define three boron fertility classes: Low, medium and high; that is 0.00–0.23, 0.23–0.58, and 0.58–3.70 mg B kg⁻¹, respectively, for B_HW, and 0.00–0.65, 0.65–1.03, and 1.03–12.70 mg B kg⁻¹, respectively, for B_M3. The third database was extracted from 130 yield responses to increasing levels of boron; it was used to define a recommendation model for boron, based on AutoML, as a function of B_M3, pH_W, the crop boron requirement (medium, high), and the type of spreading (broadcast, sidedress, foliar spraying). This model resulted in an R² of 0.63.