Agronomic Variables Research Articles

Effect of water stress on vegetative growth and leaf concentration of flavonoids in mint (mentha x piperita) under protected cultivation conditions

Medicinal plants, such as mint, contain active ingredients, which, although they are responsible for the therapeutic properties attributed to them, are also responsible for poisoning and adverse reactions that can occur if they are used in inadequate doses, high doses, or for prolonged periods. Many of these plant species have survived for millions of years thanks to their ability to produce substances that protect them from predators or extreme conditions. Even though some of these compounds (condensed tannins, phenols, alkaloids, oligosaccharides, and saponins) can produce a violent and immediate reaction, in most cases, they have a subtle effect that manifests itself with prolonged ingestion. By applying computational chemistry, the structures of bioactive molecules were revealed, representing them three-dimensionally after virtual screening. Therefore, in the project's development, the effect of water stress on the production of flavonoids in the protected mint crop was studied, using the previously measured field capacity and ultimately controlling the irrigation. This was divided into four irrigation application treatments (each 3, 6, 9, and 12 days), evaluating different agronomic variables and through a foliar analysis of the content of flavonoids present in the four treatments under study. The findings of this study, with their significant implications for medicinal plant cultivation, underscore how water stress can affect the production of flavonoids in mint. This knowledge can be directly applied to optimize cultivation practices and enhance the quality of medicinal plants, thereby benefiting the agricultural and pharmaceutical industries.

Antifungal Activity on Botrytis fabae and Response of Agronomic Variables of Plant Extracts in Faba Beans

Antifungal Activity on Botrytis fabae and Response of Agronomic Variables of Plant Extracts in Faba Beans

Commercial amino acids for yield and its components of common bean Azufrado Reyna in Northern Sinaloa

Objective: To investigate the effect of foliar commercial amino acids on yield and its components of common bean Azufrado Reyna under field conditions. Design/methodology/approach: Randomized complete block design with four replicates and four treatments (three commercial amino acids and a control). Grain yield, aboveground biomass, number of pods and normal seeds per unit surface, number of grains per pod, number of normal seeds per pod, weight of 100 seeds, and individual weight of seed were evaluated. Results: All biostimulants influenced the increase in grain yield, aboveground biomass, number of normal pods and seeds per unit surface as well as the number of seeds per pod with respect to the control. Limitations on study/implications: The study only assessed the evaluation of agronomic variables; therefore, it is necessary the measurement of morpho-physiological traits that provide evidence about the benefits of foliar aminoacids on yield and the formation of each component under field conditions. Findings/conclusions: Foliar biostimulant application had a positive effect on grain yield and some of its components as compared to the control. The number of normal seeds per normal pod was the variable that showed the highest correlation with grain yield.

Fertilization for Growth or Feeding the Weeds? A Deep Dive into Nitrogen's Role in Rice Dynamics in Ecuador.

Rice (Oryza sativa L.) is a crucial crop for employment and agricultural output and heavily reliant on family labor. This study evaluated the effects of nitrogen levels (80, 120, and 160 kg·ha-1) on weed incidence and key agronomic variables, including vegetative growth, yield, and related traits, in Ecuador's primary rice-growing regions, Guayas and Los Ríos. A split-plot randomized complete block design was implemented using two rice varieties (INIAP-FL-Elite and SFL-11) and three planting densities (20 × 30, 25 × 30, and 30 × 30 cm). Weed incidence was higher in Los Ríos, dominated by grasses (55.28%), while Cyperaceae (46.27%) prevailed in Guayas. The data analysis included non-parametric tests to identify significant treatment effects, debiased sparse partial correlations (DSPCs) to reveal key agronomic interactions, and principal component analysis (PCA) to identify influential traits, ensuring robust and normalized interpretations. Analysis of variance indicated significant effects for all variables, with vegetative growth (VG) most affected (p < 0.001). The number of panicles (NP) and days to flowering (DF) showed significant though less pronounced effects, while the panicle length (LP) and 1000-seed weight (TSB) exhibited moderate responses. The DSPCs highlighted the grains per panicle (GP) and total biomass (SB) as critical variables, with significant correlations between the days to flowering and the tiller count at 55 days (r = 0.750, p < 0.001) and between the vegetative growth and the total biomass (r = 0.678, p < 0.001). PCA explained 58.8% of the total variance, emphasizing the days to flowering, plant height, total biomass, and yield as the most influential traits. These findings underline the importance of integrated nutrient and weed management strategies tailored to Ecuador's agroecological conditions.

Agronomic potential of four cassava (Manihot esculenta Crantz) varieties grown on soils fertilized with cocoa shell compost and chicken droppings compost in Côte d’Ivoire

This study was conducted in the Lamto area to evaluate the effect of cocoa shell compost and chicken droppings compost on cassava agronomic variables. The main aim is to increase cassava production, which is in relative decline due to soil impoverishment through organic amendments. The evaluation led to the monitoring of plant development until harvest by measuring some agronomic variables of growth and yield of four varieties of cassava, Alleda agba, Bonoua, Six mois and Yacé each grown on soil fertilized with cocoa shell compost or chicken droppings compost. The experimental design used for this study was a complete randomized block design with 3 replicates. The results showed that the two fertilizers used, being rich in mineral elements, improved the agronomic variables of cassava, superior to the control. This was reflected in a high number of stems per cutting (3.62). However, the chicken droppings compost gave the best development in stem length for the Six mois variety (121.10 cm). The Bonoua variety gave the largest diameter stems (21.98 mm). This variety gave the highest number of leaves (59) and a wider span (109 cm) with wider (16.75 cm) and longer (17.96 cm) leaves. As for yields, the best was obtained with the Bonoua variety, respectively 27.13 t/ha and 35.26 t/ha with cocoa shell compost and chicken droppings compost compared with 12.77 t/ha for the control. Thus, compost and chicken droppings compost can be recommended to improve cassava productivity in Côte d'Ivoire.

Estimation of Forage Biomass in Oat (Avena sativa) Using Agronomic Variables through UAV Multispectral Imaging

Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose.

Internet of things (IoT) based saffron cultivation system in greenhouse

Saffron is the world's most expensive and legendary crop that is widely used in cuisine, drugs, and cosmetics. Therefore, the demand for saffron is increasing globally day by day. Despite its massive demand the cultivation of saffron has dramatically decreased and grown in only a few countries. Saffron is an environment-sensitive crop that is affected by various factors including rapid change in climate, light intensity, pH level, soil moisture, salinity level, and inappropriate cultivation techniques. It is not possible to control many of these environmental factors in traditional farming. Although, many innovative technologies like Artificial Intelligence and Internet of Things (IoT) have been used to enhance the growth of saffron still, there is a dire need for a system that can overcome primary issues related to saffron growth. In this research, we have proposed an IoT-based system for the greenhouse to control the numerous agronomical variables such as corm size, temperature, humidity, pH level, soil moisture, salinity, and water availability. The proposed architecture monitors and controls environmental factors automatically and sends real-time data from the greenhouse to the microcontroller. The sensed values of various agronomical variables are compared with threshold values and saved at cloud for sending to the farm owner for efficient management. The experiment results reveal that the proposed system is capable to maximize saffron production in the greenhouse by controlling environmental factors as per crop needs.

Herbage biomass predictions from UAV data using a derived digital terrain model and machine learning

AbstractMore than 70% of Switzerland's agricultural area is covered by grasslands that often exhibit highly diverse species compositions and heterogeneous growth patterns. An essential requirement for efficient and effective pasture management is the regular estimation of herbage biomass. While various methods exist for estimating herbage biomass, they are often time‐consuming and may not accurately capture the variability within pastures. This highlights the need for more efficient, accurate estimation techniques. To help improve herbage biomass estimation, we present estiGrass3D+, a Random Forest model. This model predicts pasture biomass using a digital terrain model (DTM) derived from a digital surface model (DSM) for sward height modelling, along with vegetation indices and agronomic variables from UAV images only. The model was successfully evaluated with independent test data from different sites on the Swiss central plateau, including both grazed and mown areas. Model performance on an independent validation dataset achieved a NRMSE of 20.3%, while the training dataset had an NRMSE of 21.5%. These consistent results confirm that estiGrass3D+ is both transferable and applicable to unseen data while maintaining accuracy and reliability across different datasets. The wide applicability of our method demonstrates its practicality for predicting herbage biomass under different pasture management scenarios. Additionally, our method of deriving a DTM directly from a DSM simplifies the measurement of grass sward height by UAVs, eliminating the need for prior ground control point (GCP) marking and subsequent aligning, enhancing the efficiency of herbage biomass estimation.

Quality Coefficient on Gene Differentiation and Phenotype: Clone Assessment of Saccharum officinarum LINN

The production sugarcane of superior varieties can be achieved through cross-breeding between superior parent plants based on the desired advantages. This research examines the diversity of superior clones SB04, SB11, SB19, and SB20, and identifies which clones have the highest productivity potential. The first ratoon research was carried out from August 2020 to July 2021. Data analysis used descriptive-analytic methods, regression tests, and genetic diversity assessment. Observation variables include agronomic variables and potential productivity. The findings show the genetic diversity of the superior clone SB04 showed close similarity to the heterozygous combination PS862. The superior clone SB11 showed a tendency to inherit traits similar to Cenning Elder. The superior clones SB19 and SB20 lean towards the VMC71-238 variety and the combination of PSBM01 and VMC71-238. The superior clones SB04, SB11, SB19, and SB20 produced higher variable weight, yield, and sugarcane crystal weight compared to the characteristics of their two parents. These clones—SB04, SB11, SB19, and SB20—produced high crystallization yields, ranging from 8.47 to 15.26 tonnes/ha, higher than their parent plants. SB19 has the highest yield, namely 15.26 tons/ha. Although some clones dominate crystal production, other clones inherit traits from both parents but are less dominant in overall productivity.

Effect of the Association of Levels of Potassium and Nitrogen from Coated Urea on the Forage Potential of Cactus Pear in Brazilian Savannah Soil

ABSTRACT The development of methods that aim to increase the water use efficiency in agricultural systems should be part of the objectives of research, and the cultivation of plants of lower water requirements, such as cactus pear, can be a good option. The purpose of this study was to evaluate the effect of the association of potassium (K) and nitrogen (N) levels from coated urea, in two harvest years, on the agronomic characteristics and nutritive value of Nopalea cochenillifera cv Doce. A randomized block design in a 2 × 4 × 2 factorial arrangement was adopted. The factors corresponded to two levels of K (40 kg and 60 kg ha−1 year−1), four levels of N (0, 60, 120, and 240 kg ha−1 year−1) and two harvest years (Years I and II). The plants were evaluated for growth, yield, chemical, and mineral composition, and nutritive value. The levels of K affected only plant height and K content in the plant. The agronomic variables and contents of ether extract and crude protein (CP) linearly increased along with the levels of N from the coated urea. There was a significant effect of interaction between N levels and harvest year on the contents of NDF, NDFap, NFC, fraction A+B1, Ca, and Mg. The level of 40 kg K ha−1 year−1 associated with 240 kg N ha−1 year−1 from coated urea provided the best results for the characteristics of growth, yield, and nutritive value of the cactus pear.

Sustainable Production of Forage Sorghum for Grain and Silage Production with Moisture-Retaining Polymers That Mitigate Water Stress

The objective was to evaluate the agronomic and production characteristics and the quality of forage sorghum for silage production, using a moisture-retaining polymer (MRP) in the soil during the agricultural off-season. To assess the agronomic characteristics of the forage sorghum, four treatments were used, represented by the MRP hydration intervals (Control, 0, 5, and 10 days) and four replications. The qualitative characteristics of the silage were evaluated in a 4 × 2 factorial scheme, and the same MRP hydration intervals were used for the agronomic assessment, with and without the concentrate mixture at the time of ensiling, both in a randomized block design. There were differences (p &lt; 0.05) for all morphological variables, agronomic variables, and weight constituents of forage sorghum. There was an interaction effect (p &lt; 0.05) between the MRP hydration intervals and the concentrate mixture at the time of ensiling for gas losses, silage dry matter recovery, and ether extract. There was an effect (p &lt; 0.05) due to the addition of the concentrate mixture at the time of ensiling for in vitro dry matter digestibility and total digestible nutrients. When using MRP at planting and the concentrate mixture at the time of forage sorghum ensiling, a hydration interval of every 5 days is recommended.

Development of a novel critical nitrogen concentration–cumulative transpiration curve for optimizing nitrogen management under varying irrigation conditions in winter wheat

Accurate nitrogen (N) nutrition diagnosis is essential for improving N use efficiency in crop production. The widely used critical N (Nc) dilution curve traditionally depends solely on agronomic variables, neglecting crop water status. With three-year field experiments with winter wheat, encompassing two irrigation levels (rainfed and irrigation at jointing and anthesis) and three N levels (0, 180, and 270 kg ha−1), this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration (T), providing a comprehensive analysis of the interaction between N and water availability. The Nc curves derived from both crop dry matter (DM) and T demonstrated N concentration dilution under different conditions with different parameters. The equation Nc = 6.43T−0.24 established a consistent relationship across varying irrigation regimes. Independent test results indicated that the nitrogen nutrition index (NNI), calculated from this curve, effectively identifies and quantifies the two sources of N deficiency: insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption. Additionally, the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit (Nand) and a positive correlation with relative grain yield (RGY). The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY, as indicated by larger R2 values and smaller AIC. The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status, predicting grain yield, and optimizing N fertilizer management across varying irrigation conditions. These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.

Effects of the combining ability of piquin pepper (Capsicum annuum var. Glabriusculum) from different geographical sites

Objective: To evaluate the effects of the general combining ability (GCA) and the specific combining ability (SCA) on the agronomic variables of piquin pepper (Capsicum annuum var. Glabriusculum) genotypes. Methodology: A total of 36 F1 and nine parental crosses were used as plant material. The genotypes were distributed in a completely randomized block design with three replications. Ten agronomic variables were evaluated. Results: Differences (P≤ 0.01) were found in all the evaluated variables, both in the genotypes and in GCA and SCA. Additive gene action influenced heritability, where following variables stood out: days to harvest (DTH), chlorophyll (CHL), plant height (PH), average fruit weight (AFW), fruit equatorial diameter (FED), and fruit polar diameter (FPD). One the one hand, genotypes G6 and G7 recorded the highest positive yield values for GCA, with 143.96 and 66.97 kg ha-1, respectively. On the other hand, 58% of the SCA crosses obtained favorable yield results. Meanwhile, the highest positive values were obtained by the G6xG7, G8xG9, G5xG9, G3xG4, G4xG8, and G1xG8 crosses, which recorded 427.1, 190.5, 167.4, 146.8, 129.7, and 125.7 kg ha-1, respectively. Conclusions: According to the effects of GCA and SCA on the agronomic variables of piquin pepper, the genotypes G6 and G7 can be used to develop varieties, while the G6xG7, G8xG9, and G5xG9 crosses are recommended for hybrid formation within breeding programs.

The stepwise regression analysis method for estimating sorghum production in Karangmojo

Abstract Sorghum is a multifunctional crop that can be utilized as a source of food, feed, and bioenergy. Sorghum is a plant that can adapt to land with optimal conditions, dry land with minimal nutrients and tolerant to pests and diseases. Sorghum has been widely cultivated in Indonesia, one in Gunungkidul, Yogyakarta. Sorghum production is influenced by several agronomic characteristics. The purpose of the study was to determine agronomic variables that affect sorghum production. The research was conducted in Karangmojo, Gunungkidul, Special Region of Yogyakarta, from October 2022 to March 2023. Sorghum varieties were local varieties as well as national superior varieties which are usually planted by local farmers. A total of 10 variables were analysed for their significance level on sorghum yield. The collected data were then processed using a multiple linear regression model (stepwise) using SPSS 16.0. The results showed that of the 10 agronomic variables observed, two variables contributed to the sorghum production, namely panicle weight and days to harvesting. The regression model from stepwise results is y = 8.884 + 0.036x3 – 0.062x10, with R2 = 0.754. This result indicates that the two independent variables are the main variables in determining sorghum production in Karangmojo.

Machine learning based on functional principal component analysis to quantify the effects of the main drivers of wheat yields

Assessing the response of crop yield to year-to-year climate variability at the field scale is often done using process-based models and regression techniques. Although powerful, these tools rely on strong assumptions and can lead to substantial prediction errors. In this study, we investigate the use of a flexible machine learning algorithm combining Functional Principal Component Analysis and Random Forest, to relate field scale wheat yield to local daily climate variables. Instead of computing seasonal, monthly or any other arbitrary time-frame climate averages, climate time series are decomposed by Functional Principal Component Analysis into a few data-driven basis functions, called Principal Curves, in order to summarize the dynamic of key climate variables by a limited number of interpretable components. Scores associated to these components are then used as inputs of a Random Forest algorithm for yield prediction and for analysing important factors responsible for yield variability. To evaluate our approach, we use a French national database including wheat yield data as well as climate and management practice data for 298 farm fields from 2011 to 2016 in four main producing regions. Depending on the regions, our approach can explain from 62 % to 81 % of the yield variability when both agronomic and climate variables are included, down to 56–81 % when ignoring agronomic variables and 51–74 % when ignoring climate variables. Based on a year-by-year cross-validation, RMSE ranges from 0.5 t ha−1 to 2.1 t ha−1 in non-extreme years (2012–2015). However, prediction error can reach 3.6 t ha−1 in case of exceptional weather conditions, such as those experienced in 2016 in Northern France. We find that this new approach performs in most cases better than the same machine learning algorithm using the usual time averages of climate variables, without the need to choose an arbitrary time-frame. We then show how important patterns in weather time series can be identified and how their effects on yield can be interpreted using the proposed modelling framework.

Assessment of Pistachio Shell-Based Biochar Application in the Sustainable Amendment of Soil and Its Performance in Enhancing Bell Pepper (Capsicum annuum L.) Growth

This study aimed to (a) analyze the influence of pyrolysis temperature on pistachio shell-based biochar (PSB) properties and (b) assess the PSB effect on green bell pepper (Capsicum annuum L.) growth. Pyrolysis experiments were conducted at different temperatures, determining 450 °C as optimal for soil amendment. The effect of PSB addition at different mass ratios was analyzed considering the physicochemical properties of the mixtures and the agronomic parameters of green bell pepper plants and fruits under greenhouse conditions. Results demonstrated enhancements in soil properties upon biochar incorporation, including a decrease in pH by 1%, a decrease in electrical conductivity (EC) by 4–14%, and increases in cation exchange capacity (CEC) by 4–8%, organic matter (OM) and organic carbon (OC) by 100–200%, and total nitrogen (TN) by 35%, relative to unamended soil. Agronomic variables revealed improvements, particularly during the reproductive and maturity stages, with plants treated with 1% biochar (SB1) exhibiting enhanced growth and chlorophyll content, alongside increased flower and fruit yields. Notably, the 2% biochar treatment (SB2) yielded superior fruit weight and length results, suggesting the potential for biochar to enhance both the quality and quantity of green bell pepper fruits, thereby contributing to sustainable agricultural practices.

Valorization of monovarietal Nostrana di Brisighella extra virgin olive oils: focus on bioactive compounds.

A "green breakthough" at the table due to consumer demand for healthy and sustainable foods, which aligns with the typical Mediterranean diet, has recently led to an increase in the consumption of products such as extra virgin olive oil. In fact, Italian olive cultivation, which contributes an average of 15% of world production, has seen the production of extra virgin olive oil with a value of exports that have doubled in the last 20 years. In this context, the olive oil sector of the Emilia-Romagna region (Italy), and in particular the PDO Brisighella, could achieve greater success with consumers by proposing a product obtained through sustainable agriculture that enhances the content of bioactive compounds. For these reasons, in this study, different agronomic variables are investigated in order to optimize the presence of bioactive components in extra virgin olive oil made from monovarietal Nostrana di Brisighella, namely phenolic and positive volatile compounds, thus naturally enriching this product both from health and sensory points of view. The study focuses on the volatile and phenolic fractions (derivatives of hydroxytyrosol and tyrosol) of olive oil and the positive sensory attributes (fruity, bitter and pungent) that are known to be associated with these molecules. The phenolic content is of particular interest due to the potential to support health claims. Extra virgin olive oil samples were produced from olives of the Nostrana di Brisighella cultivar; fruits were obtained through integrated pest management or organic farming and picked at four increasing indices of maturity, corresponding to four successive weeks of harvesting. These agronomic variables influenced the compositional and sensory characteristics of the extra virgin olive oils assessed, highlighting differences that likely derive from the effect of the agronomic system used, i.e., integrated pest management or organic farming.

From Space to Field: Combining Satellite, UAV and Agronomic Data in an Open-Source Methodology for the Validation of NDVI Maps in Precision Viticulture

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables.

Agronomic variables of interest for the wheat breeding program

The objective of this work was to evaluate agronomic variables of interest for the wheat breeding program. A field experiment was conducted with 15 wheat genotypes. The characters evaluated were the time in days to silking, plant height and lodging index. The experimental design used was a randomized block design with three replications, with treatments consisting of three cultivars – CD 150, Frontana and Mirante – and 12 lines obtained from crossings of the cultivar CD 150 with Frontana. The analysis of variance showed a difference between the genotypes evaluated, indicating the presence of genetic variability for all characters, with the number of days to silking and plant height being the characteristics that showed the greatest variability between the genotypes studied. In general, the lines presented a cycle ranging from early to medium, with low plants, which contributed to a lower lodging rate

Using Bokashi and Cow Urine as Organic Low-Cost Amendments Can Enhance Arugula (Eruca sativa L.) Agronomic Traits but Not Always Total Polyphenols and Antioxidant Activity

Productive traits, total polyphenols (TPC), and antioxidant activity (DPPH) of arugula submitted to the combination (or not) of cow urine and doses of bokashi were evaluated in two experimental areas. Arugula was planted in cultivation bags with 55 dm3 of capacity in protected cultivation. The treatments were bokashi doses (0, 10, 20, and 30 g) and use (or not) of cow urine diluted 1% in water. The variables evaluated were fresh leaf biomass (FLB), dry leaf biomass (DLM), plant height (PH), chlorophyll index, TPC, and DPPH. In area 1, all agronomic variables were increased at 30 g and 20 g bokashi doses. FLB was increased by 87 and 76% with 30 g of bokashi. Cow urine only increased PH. In area 2, the use of bokashi + cow urine increased FLB, DLB, and PH with a positive quadratic response. At the maximum point, the FLB was increased by 159% with 28.92 g of bokashi. Bokashi increased FLB and DLB in the two areas in all evaluated doses. For TPC, with the use of cow urine, 10 g of bokashi increased TPC by 14%. Without the use of cow urine, increases of 17 and 33% with 10 and 30 g of bokashi were observed. The 30 g of bokashi is recommended because of increased productive traits and TPC.