Patrick Veazie

Cilantro (Coriandrum sativum L.) is a popular annual culinary herb grown for its leaves or seeds. With the increase in hydroponic herb production in controlled environments, a need exists for leaf tissue nutrient standards specific to this production system. The objective of this study was to develop comprehensive foliar mineral nutrient interpretation ranges for greenhouse-grown cilantro. Cilantro plants were grown in a hydroponic sand culture system to induce and document nutritional disorders. Plants were supplied with a modified Hoagland’s solution, which was adjusted to individually add or omit one nutrient per treatment while holding all others constant. Deficiency and toxicity symptoms were photographed, after which the plant tissue was collected to determine plant dry weight and critical tissue nutrient concentrations. Nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), boron (B), iron (Fe), and zinc (Zn) deficiencies, as well as B toxicity, were induced. Deficiencies of copper (Cu), manganese (Mn), and molybdenum (Mo) were not observed during the experiment. Additional foliar tissue analysis data (n = 463) were compiled to create nutrient interpretation ranges for 12 essential elements based on a hybrid meta-analysis Sufficiency Range Approach (SRA). This approach defines ranges for deficient, low, sufficient, high, and excessive values. For each element, the optimal distribution was selected according to the lowest Bayesian Information Criterion (BIC) value. A Normal distribution best represented K and S. A Gamma distribution best represented P, Ca, Mn, and Mo, whereas a Weibull distribution best represented N, Mg, B, Cu, Fe, and Zn. These interpretation ranges, along with descriptions of typical symptomology and critical tissue nutrient concentrations, provide useful tools for both diagnosing nutritional disorders and interpreting foliar nutrient analysis results of greenhouse-grown cilantro.

Historically, leaf tissue standards have been developed and used to interpret foliar tissue analyses for the majority of horticultural crops to diagnose nutrient disorders. However, leaf tissue standards for petunia ( Petunia × hybrida ) are based on survey concentrations from small datasets. This study presents a novel method to create data-driven nutrient interpretation ranges by fitting models to provide more refined ranges of deficient, low, sufficient, high, and excessive for 11 essential elements based on 1420 data points. Data distributions were analyzed by fitting normal, Gamma, and Weibull distributions. Additionally, four machine learning algorithms J48 (a decision tree classifier), random forest (RF), which is a learning method that uses multiple decision trees, sequential minimal optimization (SMO), which is an optimization technique for support vector machines, and multilayer perceptron (MLP), which is a type of artificial neural network, were examined to determine if machine learning models could accurately classify foliar tissue analysis samples into the correct interpretation range. For all examined essential nutrients, J48 or RF yielded the highest classification accuracy compared with MLP or SMO. This study established the novel use of machine learning for interpreting petunia foliar nutrient analysis results with a higher accuracy rate than that of traditional statistical methods.

Foliar tissue analysis is an important method for diagnosing nutritional problems in greenhouse crops. Currently, gerbera foliar tissue standards are based on small sample populations and limited rate studies. This study expands prior work and presents a novel approach to creating data-driven nutrient sufficiency ranges of deficient, low, sufficient, high, and excessive, which were not previously available for gerbera. A hybrid meta-analysis sufficiency range approach (SRA) was used to determine the best fit model between Normal, Gamma, and Weibull distributions based on the lowest Bayesian information criteria This study establishes the use of a hybrid-SRA for the creation of refined interpretation ranges of gerbera foliar nutrient analysis results with a higher degree of precision than what is currently available to the greenhouse industry for diagnosing fertility status.

Micronutrient accumulation caused by low pH can lead to toxicity and have detrimental impacts on plant growth. In substrates with elevated pH, micronutrients become less available. In the first experiment, industrial hemp ( Cannabis sativa L.) growth was less at pH 3.0 and 4.0 than when pH was ≥5.0. Root growth was also observed to be inhibited at low pH levels. Leaf tissue micronutrient concentrations were higher at the lowest pH level, but no toxic accumulation occurred. In experiment 2, root growth had less mass at the lowest pH (3.1) and highest pH level (7.1). In experiment 3, substrates with three target pHs (3.8, 4.8, and 6.5) as well as three micronutrient concentrations (1X, 2X, and 4X) were examined to determine the impact of pH on micronutrient accumulation in two cultivars Cherry Wine (CW) and Sweetened (SW). Foliar micronutrient concentrations were the greatest in plants grown with pH 3.8, and the lowest concentrations occurred in plants grown at pH 6.5. Susceptibility to toxicity from micronutrient accumulation in plant tissue varied by cultivar. SW plants grown at pH 3.8 and 4X micronutrients resulted in lower leaf micronutrient toxicity symptoms, while CW plants grown under the same conditions did not. These studies suggest that C. sativa does not accumulate micronutrients to toxic levels at low pH when micros are applied within normal growing ranges, but that growth is inhibited at substrate pH < 5.0.

In the absence of controlled sufficiency studies, foliar interpretations for many horticultural crops are based on survey concentrations from small data sets. In addition, both survey and sufficiency ranges provide little interpretation regarding zones that are above or below the concentration range deemed “sufficient.” While providing a critical initial set of ranges, it was based on a limited set of data and therefore improvements in interpretation of data are needed. This study presents a novel method based on 1950 data points to create data-driven nutrient interpretation ranges by fitting models to provide more refined ranges of deficient (lowest 2.5%), low (2.5% to 25%), sufficient (25% to 75%), high (75% to 97.5%), and excessive (highest 2.5%). Data were analyzed by fitting Normal, Gamma, and Weibull distributions. Corresponding P values were calculated based on the Shapiro-Wilk test for normality for the Normal and Gamma distributions, and the Kolmogorov-Smirnov test was used for the Weibull distribution. The optimal distribution was selected based on the lowest Bayesian Information Criterion (BIC) value and visual fitness. The Weibull distribution best represented nitrogen, phosphorus, potassium, calcium, manganese, zinc, and copper, and the Gamma distribution best represented magnesium, sulfur, iron, and boron. Using the selected distributions, we propose a refined set of nutrient evaluation ranges for greenhouse-grown lettuce. These refined standards will aid growers and technical specialists in more accurately interpreting leaf tissue sample data.

Various soilless substrate components have been evaluated for many years to identify sustainable resources that do not negatively impact plant growth. Biochar is a carbon-based material that has been evaluated for use as an alternative aggregate in peat-based soilless substrates. In addition, the use of carbon adsorption for compound removal is widely used in groundwater remediation, municipal water filtration, and volatile organic compounds. Experiment one aimed to determine the impact of coarse biochar (−1 was applied to ‘Princettia Red’ and ‘Princettia White’ poinsettias ( Euphorbia pulcherrima × Euphorbia cornastra ). In Expt. 2, two different biochar particle sizes of coarse (6 mm) were examined at the same incorporation volumes as Expt. 1 and compared with a perlite-amended substrate at the same incorporation volumes. However, during Expt. 2, continual drench applications at times of irrigation of 0.0, 6.25, 12.5, 25.0, 50, and 100 μg·L ⁻¹ (ppb) paclobutrazol were applied to pansy ( Viola × wittrockiana ) ‘Matrix Blue Blotch’ and begonia ( Begonia × hybrida ) ‘Big Red Bronze Leaf’. The efficacy of paclobutrazol drenches for controlling growth in all species was unaffected by the substrate composition regarding aggregate type or aggregate incorporation rate. Thus, even though biochar is often used for bioremediation and wastewater treatment, it did not negatively impact the efficacy of paclobutrazol drenches at the concentrations used. This research suggests that when biochar is used as an amendment to peatmoss it will not influence paclobutrazol drench efficacy when incorporated up to 30% by volume for the examined species.

Greenhouse crop nutrient management recommendations based on foliar tissue testing rely heavily on human interpretation, which can result in recommendation variations and errors. Critical nutrient ranges vary for each species, and the potential for error in interpretation increases due to this complexity. Machine learning can be utilized to develop algorithms to accurately classify new information using models developed on known data from a training dataset. This study examines four different machine learning algorithms (J48, random forest [RF], sequential minimal optimization [SMO], and multilayer perceptron [MLP]) by two different cross‐validation strategies (10‐fold and 66% split) to determine if machine learning can be utilized to accurately classify foliar tissue samples within corresponding nutrient ranges. Lettuce ( Lactuca sativa L.) foliar tissue samples ( n = 1950) from a variety of controlled experiments and diagnostic samples from state and private labs were compiled and assigned to one of five nutrient ranges of deficient, low, sufficient, high, or excessive for each of 11 plant essential nutrients of interest based on Gamma or Weibull distributions. Individual machine learning algorithms were developed for each nutrient. For all examined essential nutrients, J48 or RF yielded the >98% greatest percentage correct classification when compared to MLP or SMO. This study establishes the novel use of machine learning for lettuce foliar nutrient analysis results interpretation with a higher accuracy rate than by traditional statistical methods.

Growers have been searching for alternative horticultural growing media components because of their desire to use sustainable resources. Biochar is a carbon-based material that has been evaluated for use as an alternative aggregate in peat-based soilless substrates. Additionally, silicon (Si) has been examined as a beneficial element to promote plant growth and plant quality in a variety of crops. However, there has been limited research regarding the interaction of biochar as an aggregate and Si in soilless substrates. This study aimed to determine the impact of Si and biochar on plant growth and nutrient uptake for greenhouse-cultivated hemp ( Cannabis sativa L.). Hemp plants were grown in one of 12 different substrate blends: with two rates of calcium silicate (CaSiO ₃ ), two aggregate types of biochar (medium or coarse) or perlite, and aggregate percentages of 85% peat + 15% aggregate and 70% peat + 30% aggregate. The cannabinoid concentration, plant height, diameter, or total plant biomass were similar across all substrate blends after 12 weeks of growth. Additionally, the use of CaSiO ₃ as a Si substrate amendment increased Si foliar concentrations, and the addition of biochar to peat-based mixes did not limit the Si availability for plant uptake. However, Si substrate amendments did not impact plant height, diameter, or total plant biomass. This suggests that the biochar tested during this study is suitable in peat-based substrates for C. sativa ‘BaOx’ production at rates up to 30% (by volume) in peat-based substrates with CaSiO ₃ amendments.

Many abiotic factors impact the yield and growth of Cannabis sativa (cannabis). Cannabis has been reported to be a bio-accumulator of heavy metals. For growers who are targeting floral production and other byproducts for human consumption, this is a concern. Silicon (Si) has been examined as a beneficial plant element to limit the uptake of heavy metals in a variety of crops. The objective of this study was to determine the impact of Si on heavy metal micronutrient uptake and plant growth for greenhouse-cultivated cannabis at varying Si substrate amendments. ‘Auto CBG’ plants were grown in a 70:30 peat:perlite substrate with one of three varying calcium silicate (CaSiO ₃ ) (Si) substrate amendment rates, Si _0X , Si _0.5X , or Si _1X (of 0.0, 1.04, and 2.07 kg⋅m ⁻³ CaSiO ₃ ), and one of three micronutrient fertility treatments, M _1X [0.49 boron (B), 0.19 copper (Cu), 4.02 iron (Fe), 0.99 manganese (Mn), 0.01 molybdenum (Mo), and 0.20 zinc (Zn) mg⋅L ⁻¹ ], M _2X , or M _4X , using a modified Hoagland’s solution, creating a 3 × 3 factorial. Plants grown with a Si _1X substrate amendment exhibited a significantly lower iron concentration in the foliage and root tissue when compared with those grown in a substrate without Si. After 6 weeks of growth, Si _0X plants that received a M _4X fertility rate exhibited greater foliar micronutrient concentrations of B, Mn, Zn, Fe, and Cu than plants that received a Si substrate amendment when provided a M _4X fertility rate. Additionally, lower micronutrient concentrations in floral tissue were observed in plants that received a Si substrate amendment for M _2X and M _4X when compared with plants that did not. Silicon substrate amendments had no impact on the cannabinoid concentration or plant growth metrics after 12 weeks of growth. This research suggests that using a Si substrate amendment in a greenhouse production system can limit excessive uptake and accumulation of micronutrients in the foliage, roots, and floral material of cannabis without negative impacts on plant growth or cannabinoid concentrations.