Determining the Beginning of Potato Tuberization Period Using Plant Height Detected by Drone for Irrigation Purposes

Insolation and precipitation instability associated with climate change affects plant development patterns and water demand. The potato root system and soil properties lead to water vulnerability, impacting crop yield. Regarding potato physiology, plants stop growing when the root depth stabilizes, and then the tuberization period begins. Since this moment, water supply is required. Consequently, an approach based on plant physiology may enable farmers to detect the beginning of the irrigation period precisely. Remote sensing is a fast and precise method for obtaining surface information using non-invasive data collection. The database comprises root depth (RD) and plant height (H) data collected during 2019, 2020, and 2021. This research aims to develop a dynamic approach based on remote sensing and crop physiology to accurately determine the beginning of the tuberization period, called here the irrigation critical point (ICP). The results indicate a high correlation between RD and H (>0.85) which is independent of in-field soil and relief variations > 0.95). Further, plant growth rate corroborates the correlation results with decreasing patterns in time (R2 > 0.80), independent of environmental variations. In short, it was possible to determine the ICP based on the crop growth dynamics, independently of climate variations, field placement, or irrigation system

Cultivar-specific nutritional status of potato (Solanum tuberosum L.) crops.

Gradients in the elemental composition of a potato leaf tissue (i.e. its ionome) can be linked to crop potential. Because the ionome is a function of genetics and environmental conditions, practitioners aim at fine-tuning fertilization to obtain an optimal ionome based on the needs of potato cultivars. Our objective was to assess the validity of cultivar grouping and predict potato tuber yields using foliar ionomes. The dataset comprised 3382 observations in Québec (Canada) from 1970 to 2017. The first mature leaves from top were sampled at the beginning of flowering for total N, P, K, Ca, and Mg analysis. We preprocessed nutrient concentrations (ionomes) by centering each nutrient to the geometric mean of all nutrients and to a filling value, a transformation known as row-centered log ratios (clr). A density-based clustering algorithm (dbscan) on these preprocessed ionomes failed to delineate groups of high-yield cultivars. We also used the preprocessed ionomes to assess their effects on tuber yield classes (high- and low-yields) on a cultivar basis using k-nearest neighbors, random forest and support vector machines classification algorithms. Our machine learning models returned an average accuracy of 70%, a fair diagnostic potential to detect in-season nutrient imbalance of potato cultivars using clr variables considering potential confounding factors. Optimal ionomic regions of new cultivars could be assigned to the one of the closest documented cultivar

Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada.

Statistical modeling is commonly used to relate the performance of potato (Solanum tuberosum L.) to fertilizer requirements. Prescribing optimal nutrient doses is challenging because of the involvement of many variables including weather, soils, land management, genotypes, and severity of pests and diseases. Where sufficient data are available, machine learning algorithms can be used to predict crop performance. The objective of this study was to determine an optimal model predicting nitrogen, phosphorus and potassium requirements for high tuber yield and quality (size and specific gravity) as impacted by weather, soils and land management variables. We exploited a data set of 273 field experiments conducted from 1979 to 2017 in Quebec (Canada). We developed, evaluated and compared predictions from a hierarchical Mitscherlich model, k-nearest neighbors, random forest, neural networks and Gaussian processes. Machine learning models returned R2 values of 0.49-0.59 for tuber marketable yield prediction, which were higher than the Mitscherlich model R2 (0.37). The models were more likely to predict medium-size tubers (R2 = 0.60-0.69) and tuber specific gravity (R2 = 0.58-0.67) than large-size tubers (R2 = 0.55-0.64) and marketable yield. Response surfaces from the Mitscherlich model, neural networks and Gaussian processes returned smooth responses that agreed more with actual evidence than discontinuous curves derived from k-nearest neighbors and random forest models. When conditioned to obtain optimal dosages from dose-response surfaces given constant weather, soil and land management conditions, some disagreements occurred between models. Due to their built-in ability to develop recommendations within a probabilistic risk-assessment framework, Gaussian processes stood out as the most promising algorithm to support decisions that minimize economic or agronomic risks

Tillage Management Impacts on Soil Phosphorus Variability under Maize–Soybean Rotation in Eastern Canada

Conservation tillage, including no-tillage (NT), is being used increasingly with respect to conventional tillage (CT) to mitigate soil erosion, improve water conservation and prevent land degradation. However, NT increases soil phosphorus (P) stratification, causing P runoff and eutrophication. For sustainable P management, fertilization must be balanced between P sources and actual crop demand. To reduce P losses to the environment, it is important to better understand P spatial variability in NT fields. Little is known about tillage impacts on field-scale P spatial variabi-lity in precision agriculture. This study examines tillage impacts on spatial variability of soil-avai-lable P in a maize–soybean rotation, in two commercial fields, denoted CT (10.8 ha) and NT (9.5 ha), with the aim of improving P fertilizer recommendations in Eastern Canada. NPK fertilizers were applied to the soils (Humic Gleysols) following local recommendations. Soil samples were collected in fall 2014 in regular 35 m by 35 m grids, at 0–5 and 5–20 cm depths, providing 141 and 134 geore-ferenced points for CT and NT fields, respectively. Available P and other elements were analyzed by Mehlich-3 extraction (M3), and the P saturation index (P/Al)M3 was calculated. Variability of soil-available P in both fields ranged from moderate to very high (32% to 60%). A mean (P/Al)M3 of 3% was found in both layers under CT, compared to 8% in the 0–5 cm layer and 6% in the 5–20 cm layer under NT. Relationships between P indices and other elements differed between tillage practices. This study highlights the need to improve P fertilizer recommendations in Eastern Canada

Development of Pedotransfer Functions to Predict Soil Physical Properties in Southern Quebec (Canada)

Pedotransfer functions (PTFs) are empirical fits to soil property data and have been used as an alternative tool to in situ measurements for estimating soil hydraulic properties for the last few decades. PTFs of Saxton and Rawls, 2006 (PTFs’S&R.2006) are some of the most widely used because of their global aspect. However, empirical functions yield more accurate results when trained locally. This study proposes a set of agricultural PTFs developed for southern Quebec, Canada for three horizons (A, B, and C). Four response variables (bulk density (ρb), saturated hydraulic conductivity (Ksat), volumetric water content at field capacity (θ33), and permanent wilting point (θ1500)) and four predictors (clay, silt, organic carbon, and coarse fragment percentages) were used in this modeling process. The new PTFs were trained using the stepwise forward regression (SFR) and canonical correlation analysis (CCA) algorithms. The CCA- and SFR-PTFs were in most cases more accurate. Θ1500 and at θ33 estimates were improved with the SFR. The ρb in the A horizon was moderately estimated by the PTFs’S&R.2006, while the CCA- and SFR-PTFs performed equally well for the B and C horizons, yet qualified weak. However, for all PTFs for all horizons, Ksat estimates were unacceptable. Estimation of ρb and Ksat could be improved by considering other morphological predictors (soil structure, drainage information, etc.)

Tillage Management Impacts on Soil Phosphorus Variability under Maize–Soybean Rotation in Eastern Canada

Conservation tillage, including no-tillage (NT), is being used increasingly with respect to conventional tillage (CT) to mitigate soil erosion, improve water conservation and prevent land degradation. However, NT increases soil phosphorus (P) stratification, causing P runoff and eutrophication. For sustainable P management, fertilization must be balanced between P sources and actual crop demand. To reduce P losses to the environment, it is important to better understand P spatial variability in NT fields. Little is known about tillage impacts on field-scale P spatial variabi-lity in precision agriculture. This study examines tillage impacts on spatial variability of soil-avai-lable P in a maize–soybean rotation, in two commercial fields, denoted CT (10.8 ha) and NT (9.5 ha), with the aim of improving P fertilizer recommendations in Eastern Canada. NPK fertilizers were applied to the soils (Humic Gleysols) following local recommendations. Soil samples were collected in fall 2014 in regular 35 m by 35 m grids, at 0–5 and 5–20 cm depths, providing 141 and 134 geore-ferenced points for CT and NT fields, respectively. Available P and other elements were analyzed by Mehlich-3 extraction (M3), and the P saturation index (P/Al)M3 was calculated. Variability of soil-available P in both fields ranged from moderate to very high (32% to 60%). A mean (P/Al)M3 of 3% was found in both layers under CT, compared to 8% in the 0–5 cm layer and 6% in the 5–20 cm layer under NT. Relationships between P indices and other elements differed between tillage practices. This study highlights the need to improve P fertilizer recommendations in Eastern Canada

Nitrogen source and rate effects on residual soil nitrate and over-winter NO3-N losses ‎for irrigated potatoes on sandy soils

Residual soil NO3-N (RSN) is susceptible to loss during the non-growing season. This 5 yr study investigated the effects of three N fertilizer sources [ammonium nitrate (AN), ammonium sulfate (AS), and polymer-coated urea (PCU)] applied at four rates (60, 120, 200, and 280 kg N ha−1) plus an unfertilized control on RSN following potato production and on overwinter NO3-N changes in an irrigated sandy soil in Quebec, Canada. Composite soil samples were collected at the 0–15, 15–30, 30–60, and 60–90 cm depths immediately after potato harvest in fall and again in the following spring from 2008 to 2012. Residual soil NO3-N content within the 0–30 cm depth (RSN0–30) was highly correlated with the RSN content in the 0–90 cm depth (RSN0–90), indicating that RSN0–30 can be used as an indicator of soil profile NO3-N accumulation. Overall, RSN0–90 increased with fertilizer N application rate, particularly for above the minimum fertilizer N rate required to maximize yield (Nmax), and was generally higher for years with greater pre-plant soil NO3-N. The split application of AN and AS resulted in lower RSN0–90 than the single application of PCU at above Nmax. Overwinter losses of soil NO3-N were generally increased with increasing RSN0–90 in fall. The results suggest that reducing the fertilizer N rate is more important than the choice of N source in managing RSN.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Proximal and Remote Sensing Data Integration to Assess Spatial Soil Heterogeneity in Wild Blueberry Fields

Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (ECa), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil attributes and evaluate the potential of site-specific management (SSM) of nutrients. Elevation and ECa at multiple depths were collected from two experimental fields (referred as FieldUnd, FieldFlat) in Normandin, Quebec, Canada. Soil samples were collected at two depths (0–0.05 m and 0.05–0.15 m) and analyzed for a range of soil properties. Statistical analyses of fruit yield, soil, and sensor data were used to characterize within-field variability. Fruit yield showed large variability in both fields (CVUnd = 54.4%, CVFlat = 56.5%), but no spatial dependence. However, several soil attributes showed considerable variability and moderate to strong spatial dependence. Elevation and the shallowest depths of both the Veris (0.3 m) and DUALEM (0.54 m) ECa sensors showed moderate to strong spatial dependence and correlated significantly to most soil properties in both study sites, indicating the feasibility of SSM. In place of management zone delineation, a quadrant analysis of the shallowest ECa depth vs. elevation provided four sensor combinations (scenarios) for theoretical field conditions. ANOVA and Tukey–Kramer’s post hoc test showed that the greatest differentiation of soil properties in both fields occurred between the combinations of high ECa/low elevation versus low ECa/high elevation. Vegetation indices (VIs) obtained from satellite data showed promise as a biomass indicator, and bare spots classified with satellite imagery in FieldUnd revealed significantly distinct soil properties. Combining proximal and multispectral data predicted within-field variations of yield-determining soil properties and offered three theoretical scenarios (high ECa/low elevation; low ECa/high elevation; bare spots) on which to base SSM. Future studies should investigate crop response to fertilization between the identified scenarios

Proximal and Remote Sensing Data Integration to Assess Spatial Soil Heterogeneity in Wild Blueberry Fields

Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (ECa), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil attributes and evaluate the potential of site-specific management (SSM) of nutrients. Elevation and ECa at multiple depths were collected from two experimental fields (referred as FieldUnd, FieldFlat) in Normandin, Quebec, Canada. Soil samples were collected at two depths (0–0.05 m and 0.05–0.15 m) and analyzed for a range of soil properties. Statistical analyses of fruit yield, soil, and sensor data were used to characterize within-field variability. Fruit yield showed large variability in both fields (CVUnd = 54.4%, CVFlat = 56.5%), but no spatial dependence. However, several soil attributes showed considerable variability and moderate to strong spatial dependence. Elevation and the shallowest depths of both the Veris (0.3 m) and DUALEM (0.54 m) ECa sensors showed moderate to strong spatial dependence and correlated significantly to most soil properties in both study sites, indicating the feasibility of SSM. In place of management zone delineation, a quadrant analysis of the shallowest ECa depth vs. elevation provided four sensor combinations (scenarios) for theoretical field conditions. ANOVA and Tukey–Kramer’s post hoc test showed that the greatest differentiation of soil properties in both fields occurred between the combinations of high ECa/low elevation versus low ECa/high elevation. Vegetation indices (VIs) obtained from satellite data showed promise as a biomass indicator, and bare spots classified with satellite imagery in FieldUnd revealed significantly distinct soil properties. Combining proximal and multispectral data predicted within-field variations of yield-determining soil properties and offered three theoretical scenarios (high ECa/low elevation; low ECa/high elevation; bare spots) on which to base SSM. Future studies should investigate crop response to fertilization between the identified scenarios