ABSTRACT Micronutrients play a crucial role in supporting plant growth and development, even though they are required in minimal quantities. Deficiencies in certain micronutrients can have a significant impact on plant development leading to reduced yields and lower-quality crops. Chelation, a vital process in plant nutrition, involves the formation of stable complexes of micronutrients bound to organic molecules, offering a promising solution to enhance their effectiveness in plant nutrition and mitigate deficiencies. Chelates contribute to the plant’s stability and uptake of micronutrients, thus alleviating the effects of inadequacies. Chelating agents can be broadly categorized as synthetic or natural compounds. Synthetic chelators encompass substances like EDTA (Ethylene Diamine Tetra Acetic Acid), DTPA (Diethylene Triamine Penta Acetic Acid), and EDDHA (Ethylene-Diamine-di-O-Hydroxy Phenylacetic Acid). In contrast, natural chelators include amino acids, peptides, and organic acids. The mechanisms of chelation in plants and soil involve intricate interactions among chelates, micronutrients, and soil components, facilitating the uptake and translocation of micronutrients within plant tissues. Although, natural chelates exhibit superior stability and compatibility with soil microbiota in addition to synthetic chelates still promotes sustained nutrient availability. Through this review, the efficacy of chelated micronutrients in enhancing plant growth, yield, and overall nutritional quality is investigated, shedding light on their potential to address deficiencies and optimize agricultural productivity.

ABSTRACT One of the alternatives for reducing nitrogen fertilization is the use of plant growth-promoting bacteria (PGPB), such as Pseudomonas fluorescens and Azospirillum brasilense, which can provide a sustainable form of production, with increased profitability for rural producers, because bacteria are a low-cost technology compared to fertilizer. In view of the above, the objective of this study was to evaluate the effect of inoculation with plant growth-promoting bacteria (P. fluorescens and A. brasilense) combined with the supply or not of Si and doses of nitrogen (N) in zoysia grass sod production area. The experimental design was in randomized blocks with 12 treatments arranged in a 3 × 2 × 2 factorial scheme, with four replications, in plots of 9 m2. The treatments were: two inoculations with PGPB (A. brasilense and P. fluorescens) and non-inoculated, combined with two doses of N, with or without supply of Si through fertilization (through the commercial fertilizer known as Potasil). There was an increase in the shoot dry matter with the supply of Si, the standard dose of N and the inoculation with PGPB. The standard dose of N promoted an increase in the content of nitrogen (N), phosphorus (P) and potassium (K) in the shoot of Zoysia japonica. There was an effect of inoculation with plant growth-promoting bacteria combined with the supply of silicon and N rates in the zoysia grass production area, where the use of Pseudomonas combined with the application of Si with 75% of the N dose is recommended.

ABSTRACT Nutritional diagnosis, a technique used to maximize the economic yield of agricultural holdings and to protect the environment, is the focus of this study. Nutrient availability is one of the critical limiting factors in maize grain yield. The study, which aimed to establish and compare the nutritional standards obtained by the Compositional Nutrient Diagnosis (CND) method in maize grown in tropical soils in a semi-arid region, is based on a thorough examination of a database containing information from 198 experimental plots collected between 2011 and 2019 in the semi-arid region of Ceará, Brazil. The evaluation methodology was the multivariate CND calculation method. The mathematical models used to analyze the relationship between the nutrient’s CND-r2 index and leaf concentrations revealed coefficients of determination (R2) for nitrogen (N) and magnesium (Mg) of 0.36 and 0.66, respectively. The other values exceeded 70% (R2 ≥ 0.70), while phosphurus (P), calcium (Ca), cupper (Cu), iron (Fe), and manganese (Mn) showed values above 90% (R2 ≥ 0.90). These results not only highlight the high accuracy of these models in studying the interactions between nutrient coefficients and their leaf concentrations but also reassure the reader about the reliability of the research. The study’s thoroughness instills confidence in the research’s validity.

ABSTRACT The higher productivity from soil can be achieved through Wireless Sensor Networks (WSNs) technology; for this reason, the implementation of WSNs in precision agriculture is increasing day by day. Among the different technologies for crop monitoring, WSNs are recognized as a powerful option for collecting and processing data in the agricultural domain, with low cost and low energy consumption. The ultimate aim of this paper is to predict the precise values of soil nutrients nitrogen, phosphorus, and potassium (NPK) using a machine learning (ML)-based mathematical model formulation. The proposed system will use Global System for Mobile Communications (GSM) technology to automatically monitor soil parameters. It transfers soil data over the mobile network using a GSM modem. In the hardware part, sensor node units were developed to fetch and load data through the energy-efficient GSM, utilizing a bi-directional multi-level text messaging option to improve the alarm system’s efficiency. The system contains sensors such as potential of hydrogen and humidity (PH), which are used to monitor soil data collected from the sensors by the microcontroller. Additionally, as a novel contribution, machine learning based on regression models is applied to the NPK data. A novel data interpolation approach is proposed for data oversampling. The R2 values are compared, achieving nearly 99.5% accuracy in all cases. This paper focuses on various challenges and scalability issues encountered in research and discusses different results obtained by monitoring various soil parameters in a real-time agricultural environment.

ABSTRACT Nanoparticle-based methods can compensate for yield and quality loss of crops affected by drought. The current study, performed in a factorial experiment based on a completely randomized design, addressed to evaluate the effect of titanium dioxide nanoparticles (TiO2 NPs) at 0, 10, 20, and 30 mg L−1 under three irrigation regimes (full irrigation, partial root drying (PRD), and sustained deficit irrigation (SDI)) on Fragaria × ananassa cv. Camarosa. Results revealed that the PRD stress had more adverse effects on F. ananassa cv. Camarosa than SDI stress. Assessment of the behavior of TiO2 NPs in this study elucidated that mean productivity, yield stability index, and fruit number in plants grown under full irrigation increased when treated with 10 mg L−1 TiO2 NPs. Under the deficit irrigation, including PRD and SDI, all levels of TiO2 NPs mitigated mean productivity and yield stability index by ameliorating the fruit number and water use efficiency (WUE) and decreasing transpiration. Flowering and fruit set times were reduced by TiO2 NPs and deficit irrigation while their periods were enhanced by ones. It seems that when the strawberry was exposed to TiO2 NPs exhibited approximately drought tolerance. These nanoparticles ameliorated photosynthesis and mineral uptake and allocated dry matter to the root. These alterations can contribute to crop production in deficit irrigation strategies.

ABSTRACT Determining total organic carbon and its stock and quantifying the chemical fractions of soil organic matter can be used as indicators of the quality of the soil management used in a cropping area. This study aimed to quantify soil organic matter’s carbon stock and chemical fractions in different soil management systems used in the western Bahian Cerrado, Brazil, and compare them to a native Cerrado area. Ten microregions (Alto Horizonte, Anel da Soja, Bela Vista, Cascudeiro, Coaceral, Novo Paraná, Panambi, Placas, Roda Velha, Roda Velha de Baixo), three soil management systems [tillage system (TS), no-tillage systems (nTS), native Cerrado (NC)], and two soil layers (0–0.1 and 0.1–0.2 m) were studied. Soil bulk density (Sd), soil organic matter (SOM), total organic carbon (TOC), SOM quantification (qSOM), equivalent carbon stock (EqCs), humin (HF), humic acid (HAF) and fulvic acid (FAF) fractions from SOM were evaluated. The results indicated that Sd in all evaluated microregions was below the critical upper limit that would restrict plant root development. SOM, TOC, EqCs, and qSOM revealed increasing results following the TS, nTS, and NC order. The NC area presented the highest soil organic fraction contents (HF, HAF, and FAF). The microregions with better soil quality for all evaluated parameters were Coaceral and Cascudeiro; the lowest soil quality was observed in Alto Horizonte. The present study indicates that conservation agricultural management, such as the no-tillage system, improves the structure and composition of SOM parameters over time.

ABSTRACT Wheat cultivated under conservation agriculture faces challenges in nutrient management due to altered soil dynamics and heavy residue loads. This often necessitates excessive nutrient applications to address the nutritional demands of the crop. Conducted at the Norman E. Borlaug Crop Research Centre during the winter seasons of 2020–21 and 2021–22, this study explored the effects of different nitrogen (N) application strategies on the ‘HD 2967’ wheat cultivar planted amidst rice residues using a super-seeder. The Nutrient Expert® (NE) combined with LCC-based N top dressing (NE+LCC) demonstrated the most optimal nutrient balance, leading to a 5.9% increase in grain yield and a 42.8% increase in dry matter accumulation compared to traditional methods. The composite SSNM treatments like NE+SPAD and NE+LCC recorded 48.9% and 41.9% higher SPAD values, respectively, as compared to RDF. The NE treatments resulted in significant savings, with a reduction of 60% in phosphorus usage and 5% in potassium. Economically, the NE treatment proved superior, showcasing a 12.7% higher net return compared to the RDF approach. The study concludes that site-specific nutrient management, facilitated by advanced tools like LCC and SPAD, when integrated with software-driven strategies, boosts wheat yield and fosters sustainable farming practices. This approach minimizes environmental impacts by optimizing nutrient applications and aligns with the sustainability development goal of responsible consumption and production.

ABSTRACT Andisols are the least extensive soil order, accounting for less than 1% of Earth´s surface. Chile occupies 50% of the country´s land area for cereal production and is of great importance to agriculture. However, few studies have investigated the performance of cereal production under intercropping in P-deficient Andisols. The objective of this study was to evaluate the effects of two different Fabaceae species and wheat in an intercropping system on root morphology and soil properties. A 2-year field experiment was conducted using a completely randomized block experimental design with a factorial arrangement with two different phosphorus levels and cropping systems (wheat monoculture, wheat/lupine, and wheat/chickpea intercropping). Bulk soil samples were collected from a field that had been cultivated with wheat. Chemical properties, basal soil respiration, and enzymatic activity were measured. The morphological characteristics of wheat roots and crop yield were also determined. According to the multiple linear regression model (p < .001), this relative yield was related to an increase in phosphatase activity and root biomass. Furthermore, the Land Equivalent Ratio (LER) of the wheat/lupine intercrop surpassed 1 in both seasons, indicating improved soil and nutrient utilization. In contrast, the wheat/chickpea intercrops had LER values lower than one during the second season. This confirmed that wheat/lupine intercropping is a recommended practice for enhancing ecosystem services and agricultural production in Andisols.

ABSTRACT Global warming delays the harvest of kharif crops, which delay the subsequent sowing of Indian mustard, especially in northwestern India and also causes dry winters that exacerbate challenges for the timely sowing of rabi-crops. To check this, a field experiment was conducted during the Rabi-seasons of 2018–19 and 2019–20 to assess the influence of sowing dates, irrigation, and fertilizer levels under late-sown conditions. A split-plot design was used with two sowing dates, two irrigation levels and four fertilizer levels (87.5%, 100%, 112.5% and 125% RDF; Recommended-fertilizers-dose). Crop sown in the 4th week of November significantly reduced seed (46.79 and 38.77%) and stover yield (43.72 and 41.62%) as compared to the 2nd week of November sowing during 2018–19 and 2019–20, respectively. Uptake of NPK by seed and stover was higher with the 2nd week of November sowing. The 2nd week of November sowing resulted in significant increase in water productivity by 63.72% and Benefit cost ratio (B:C) by 75.45% over the 4th week of November sowing. Irrigation at flowering stage increased the seed yield, water productivity and B:C by 11.94%, 1.72% and 9.17%, respectively, as compared to No post-sown irrigation. The biological yield of the Indian mustard increased with every increase in fertilizer doses but the response was significant up to 112.5% RDF (90 kg N, 33.75 kg P2O5 and 22.5 kg K2O ha−1). Application of 112.5% RDF increased the seed, stover yield, water productivity and B:C by 16.17%, 9.29%, 15.66% and 11.27%, respectively, over 87.5% RDF.

ABSTRACT Soil is an important resource worldwide with diverse physical, chemical, and biological properties. These properties vary from place to place because ecological variables such as temperature, moisture, and land use vary across different ecosystems. Soil quality has declined, which has led to increased demand for food, which poses significant problems in enhancing agricultural production and promoting environmental sustainability. The traditional methods for analyzing soil nutrients are labor-intensive, tedious, and expensive. The soil properties were effectively analyzed via artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) applications, to explain challenging problems with high accuracy and robustness. To interpret multidimensional data inputs derived from agro-industries and provide farmers with relevant information about crop conditions and soil management. AI can increase crop production by optimizing soil nutrient management. With artificial intelligence technology, farmers can identify potential deficits in soil quality, while Machine learning technologies, such as random forests (RF), support vector machines (SVMs), and Artificial and Deep neural networks (ANN, DNN), were used to generate predictive models on the basis of available soil data and auxiliary ecological variables. This review provides a detailed overview of the diverse AI tools and models used for the detection of various soil properties.