Wisconsin and Quebec are the world leading cranberry-producing regions. Cranberries are grown in acidic, naturally low-fertility sandy beds. Cranberry fertilization is guided by general soil and tissue nutrient tests in addition to yield target and vegetative biomass. However, other factors such as cultivar, location, and carbon and nutrient storage impact cranberry nutrition and yield. The objective of this study was to customize nutrient diagnosis and fertilizer recommendation at local scale and for next-year cranberry production after accounting for local factors and carbon and nutrient carryover effects. We collected 1768 observations from on-farm surveys and fertilizer trials in Quebec and Wisconsin to elaborate a machine learning model using minimum datasets. We tested carryover effects in a 5-year Quebec fertilizer experiment established on permanent plots. Micronutrients contributed more than macronutrients to variation in tissue compositions. Random Forest model related accurately current-year berry yield to location, cultivars, climatic indices, fertilization, and tissue and soil tests as features (classification accuracy of 0.83). Comparing compositions of defective and successful tissue compositions in the Euclidean space of tissue compositions, the general across-factor diagnosis differed from the local factor-specific diagnosis. Nutrient standards elaborated in one region could hardly be transposed to another and, within the same region, from one bed to another due to site-specific characteristics. Next-year yield and nutrient adjustment could be predicted accurately from current-year yield and tissue composition and other features, with R2 value of 0.73 in regression mode and classification accuracy of 0.85. Compositional and machine learning methods proved to be effective to customize nutrient diagnosis and predict site-specific measures for nutrient management of cranberry stands. This study emphasized the need to acquire large experimental and observational datasets to capture the numerous factor combinations impacting current and next-year cranberry yields at local scale.

High berry yield and quality of conventionally and organically grown cranberry stands require proper nutrient sources and dosage. Our objective was to model the response of cultivar “Stevens” to N, P, K, Mg, Cu, and B fertilization under conventional and organic farming systems. A 3-year trial was conducted on permanent plots at four production sites in Quebec, Canada. We analyzed yield predictors, marketable yield, and fruit quality in response to fertilization and soil properties. Cranberry responded primarily to nitrogen fertilization and, to a lesser extent, to potassium. Berry yield was closely related to the number of fruiting uprights (r = 0.92), berry counts per fruiting upright (r = 0.91), number of reproductive uprights (r = 0.83), and fruit set (r = 0.77). Nitrogen increased berry yield nonlinearly but decreased berry firmness, total anthocyanin content (TAcy), and total soluble solids content (°Brix) linearly, indicating a trade-off between berry yield and quality. Fertilizer dosage at a high-yield level ranged between 30 and 45 kg N ha−1 in both conventional and organic farming systems. Slow-release fertilizers delayed crop maturity and should thus be managed differently than ammonium sulfate. Berry weight increased with added K. Redundancy analysis showed a close correlation between marketable yield, berry quality indices, and soil tests, especially K and Ca, indicating the need for soil test calibration.

Cranberry (Vaccinium macrocarpon Ait.) is an ammophilous plant grown on acid soils (pH 4.0 -5.5). Elemental sulfur is commonly applied at a recommended rate of 1120 kg S ha −1 per pH unit to acidify cranberry soils, potentially impacting the plant mineral nutrition. The general recommendation may not fit all conditions encountered in the field. Our objective was to develop an equation to predict the sulfur requirement to reach pH water of 4.2 to tackle nitrification in acidic cranberry soils varying in initial pH values, and to measure the effect of elemental sulfur on the mineral nutrition and the performance of cranberry crops. A 3-yr experiment was designed to test the effect of elemental sulfur on soil and tissue tests and on berry yield and quality. Four S treatments (0, 250, 500 and 1000 kg S ha −1 ) were established on three duplicated sites during two consecutive years. We ran soil, foliar tissue, berry tissue tests, and measured berry yield, size, anthocyanin content (TAcy), Brix, and firmness. Nutrients were expressed as centered log ratios to reflect nutrient interactions. Results were analyzed using a mixed model. Soil Ca decreased while soil Mn and S increased significantly (p ≤ 0.05). Sulfur showed no significant effects on nutrient balances in uprights. The S impacted negatively berry B balance, and positively berry Mn and S balances. A linear regression model relating pH change to S dosage and elapsed time (R 2 = 0.53) showed that to reach pH water of 4.2 two years after S application, 250 -1000 kg S ha −1 could be applied depending on initial soil pH value. The stratification of surface-applied elemental S in the soil profile should be further examined in relation to plant rooting and nutrient leaching.