Soil Macronutrients Research Articles

A novel on-line dual sensing system for soil property measurement and mapping

Real-time assessment of within-field soil fertility variation is crucial for making informed and sustainable decisions, like crop nitrogen (N) fertilization. Two commonly used soil sensors i.e., visible-near-infrared (vis-NIR) spectroscopy and ion-selective electrodes (ISE) have been reported to successfully estimate various soil macronutrients and nitrate-N, respectively, however, their integrated use for mapping a full-scale soil fertility variation has not been taken into consideration to date. The aim of this study is to use an integrated on-line dual-sensor system of vis-NIRS and ISE to estimate and map within-field variation in multiple soil fertility parameters including pH, organic carbon (OC), extractable- phosphorus (P), potassium (K), calcium (Ca) and moisture content (MC) and nitrate-N. The sensing system was mounted to the three-point linkage of a tractor and surveyed two arable fields in Belgium. Partial least squares regression models for vis-NIRS sensor were calibrated and validated, while a linear regression model was established for validation of the ISE sensor. Geostatistical surface maps for on-line measured soil attributes were generated using the inverse distance weighting and ordinary kriging methods for ISE data and vis NIR, respectively. The linear correlation confirms a very high similarity between ISE-measured nitrate-N and laboratory-analyzed nitrate-N (coefficient of determination (R2) = 0.93). Besides, the vis-NIRS demonstrates very good prediction accuracies for all the fertility attributes with R2 = 0.70-0.78, root mean square error (RMSE) =0.52-2.46 %, residual of prediction deviation (RPD) = 1.89 – 2.13 and the ratio of the performance to interquartile distance (RPIQ) = 1.72 – 6.56. Validation between laboratory and on-line measured soil maps also shows quite comparable spatial distribution patterns. Therefore, the proposed dual on-line sensor system has a high potential to estimate and map within-field spatial fertility distributions including nitrate-N, offering a basis for sustainable management decisions for precision soil and crop production.

Soil sampling design matters - Enhancing the efficiency of digital soil mapping at the field scale

Optimisation of sampling design (methods chosen to select the samples) and sample size (number of samples) remains a key challenge in digital soil mapping, especially in the area of precision farming with the expected economic benefits from the introduction of new technologies. As the existing information is available in the form of relevant environmental covariates, its combination with non-parametric machine learning techniques requires careful planning from the initial field sampling to the final production of digital soil maps. The aim of this study is to compare widely used covariate-wise sampling designs combined with variable sample sizes for supervised prediction of common soil drivers of agricultural productivity (pH, soil organic carbon, soil macronutrients) in a real case study of a field (35 ha) with heterogeneous soil properties. From a total of 200 samples, we evaluated different sample sets where 10, 30 and 60 field samples were selected by conditioned Latin Hypercube Sampling (cLHS) and Feature Space Coverage Sampling (FSCS) to calibrate random forest (RF) models. The evaluation was performed on independently in-situ sampled test points. In addition to these datasets, we also compared the investigated methods with Simple Random Sampling (SRS) in a numerical benchmark experiment with increasing sample size, comparing the global accuracies of the predicted maps on the test points, but using interpolated maps as the artificial true population for each soil characteristic. The results of the study in both the field experiment and the numerical experiment showed slightly better results for the FSCS method, especially when the number of samples was small. At smaller training sample sizes, the risk of insufficiently accurate prediction models was slightly lower for FSCS and the difference decreased as the sample size increased. Nevertheless, sample size proved to be the most important factor in the accuracy of RF models, regardless of the sampling technique. The results suggest that a sample size between 18 and 30 training samples (0.6 to 1 sample ha−1) seems plausible for covariate-wise predictions using RF at field scale in our case study. The relative importance of each auxiliary variable for each RF calibration was also assessed for the field experiment. The results showed that the additional introduction of spatial proxies overshadowed the importance of other covariates, but only significantly improved the model calibration at larger sample sizes. The calibrated models without spatial proxies showed the strongest effect of remotely sensed surface characteristics.

Integrated Impact of Olive Mill Wastes and Semi-Circular Bunds on Soil Properties and Rainwater Harvesting Efficiency in Egypt’s Northwestern Coastal Zone

ABSTRACT The productivity of olive in the northwestern coastal zone of Egypt is significantly constrained by drought and low organic matter in the soil. This study addresses these challenges by exploring the synergistic effects of olive mill wastes (OMW) application and micro-catchment water harvesting techniques on soil properties, runoff, moisture storage, and rainwater harvesting efficiency. Field experiments were conducted over two winter seasons, employing different micro-catchment structures (semi-circular and triangular bunds) and OMW applications. The results revealed that the integration of semi-circular bunds with OMW application (T4) substantially increased rainwater harvesting efficiency by 67.7% and 46.4% in the first and second seasons, respectively. Additionally, T4 and T5 exhibited reduced runoff by 47.8% and 41.3% in the first season and 49.4% and 38.9% in the second season, respectively. T4 significantly enhanced soil organic matter, soil macronutrients (N, P, and K), and micronutrients (Fe, Mn, Zn, and Cu) compared to the control (T1). The study highlights the effectiveness of T4 in improving soil moisture, reducing soil erosion, and enhancing soil fertility. This integrated approach involving OMW application within a semi-circular bund as a water harvesting system proves to be a promising strategy for sustainable olive cultivation in arid conditions.

Macro-Nutrient Prediction of Paddy Field Soil Using Artificial Neural Network and NIR Spectroscopy

Understanding soil fertility, influenced by macronutrients like nitrogen, phosphorus, and potassium, is essential for adaptive agriculture implementation based on various soil conditions. Near-infrared spectroscopy technology provides non-destructive, rapid soil property measurements without chemicals, applicable both in-field and in-laboratory. However, the wide NIR spectrum range and neural network complexities can hinder Artificial Neural Network (ANN) training and inference, leading to time and resource inefficiency, especially without sophisticated computing devices. This study examines data reduction methods to enhance ANN performance in predicting soil macronutrients using NIR spectra. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were applied to select wavelengths from the 1000–2500 nm for ANN input, comparing their performance. About 237 NIR reflectance data from paddy soil were transformed into absorbance data. MLR used forward selection to identify wavelengths with correlations higher than 0.9, while PCA selected wavelengths corresponding to the loading factor peaks for each principal component. These selected wavelengths served as inputs for the ANN model. The ANN’s performance was assessed using correlation and determination coefficients, RMSE, RPD, and model consistency. For nitrogen, the PCA+ANN model with reflectance spectra performed better (RPD 2.4-4.8) than the MLR+ANN model (RPD 2.2-2.6) using fewer wavelengths (5-9 for PCA+ANN vs. 9-12 for MLR+ANN). For phosphorus estimation, the PCA+ANN model also excelled (RPD 2.3-7.0 vs. 2.3-2.4) with fewer wavelengths (4-7 vs. 7). For potassium estimation, the PCA+ANN model showed superior performance (RPD 4.3-9.5 vs. 4.2-4.4), using the same number of wavelengths (4-8 vs. 4-6).

Analysis of Macronutrients in Soil Using Impedimetric Multisensor Arrays.

The need to increase food production to address the world population growth can only be fulfilled with precision agriculture strategies to increase crop yield with minimal expansion of the cultivated area. One example is site-specific fertilization based on accurate monitoring of soil nutrient levels, which can be made more cost-effective using sensors. This study developed an impedimetric multisensor array using ion-selective membranes to analyze soil samples enriched with macronutrients (N, P, and K), which is compared with another array based on layer-by-layer films. The results obtained from both devices are analyzed with multidimensional projection techniques and machine learning methods, where a decision tree model algorithm chooses the calibrations (best frequencies and sensors). The multicalibration space method indicates that both devices effectively distinguished all soil samples tested, with the ion-selective membrane setup presenting a higher sensitivity to K content. These findings pave the way for more environmentally friendly and efficient agricultural practices, facilitating the mapping of cropping areas for precise fertilizer application and optimized crop yield.

Mapping Soil-crop Nutrient Dynamics in Continuous Cotton Cultivation Areas of Sangareddy District, Telangana

A soil-crop nutrient survey was conducted in Sangareddy district, Telangana during Rabi, 2023 to study the fertility status of cotton growing soils at 0-15 cm and 15-30 cm depth. Georeferenced soil and leaf samples were collected from 112 sites covering 23 mandals during flowering stage. Soil physical and physico-chemical properties and cotton leaf nutrient content were analysed. In the study area, bulk density, sand, silt and clay content ranged from 1.36-1.66 Mg m-3, 41.84-78.02 %, 6.74-29.60 % and 10.76-42.76 %, respectively. The soils were strongly acidic to slightly alkaline in reaction (4.25-7.98; 4.44-7.97), non-saline (0.026-0.254 dS m-1; 0.020-0.241 dS m-1) and low to high in organic carbon (2.03 to 11.52 g kg-1; 1.13 to 8.24 g kg-1) at 0-15 cm and 15-30 cm depths, respectively. Soil Nutrient Status: Available nitrogen, phosphorus, potassium and exchangeable magnesium contents ranged from 101 to 448 kg ha-1, 7 to 41 kg ha-1, and 154 to 763 kg ha-1 at 0-15 cm depth, respectively while 91 to 319 kg ha-1, 4 to 39 kg ha-1, and 142 to 730 kg ha-1 at 15-30 cm depth, respectively. Crop Nutrient Status: Total nitrogen, phosphorus, potassium and magnesium contents in the collected cotton leaves ranged from 0.53 to 3.64 %, 0.12 to 0.68 %, 0.80 to 1.84 % and 0.27 to 0.92 %, respectively. Soil Fertility Mapping: Soil fertility maps were prepared considering the low/deficient, medium and high/sufficient range of soil macronutrients (N, P, K and Mg) at the depth of 0-15 cm under QGIS 3.36.1. Soil fertility maps showed that soils of the study area were mostly low in available nitrogen, medium in available phosphorus, high in available potassium and sufficient in exchangeable magnesium. Regular soil testing is crucial to monitor the variations in nutrient levels and formulation of appropriate management strategies to address it.

Influence of planting dates and fertilizer modules on yield of chrysanthemum and soil health

BackgroundOptimum planting date and appropriate fertilizer module are essential facets of chrysanthemum cultivation, to enhance quality yield, and improve soil health. A field-based study was undertaken over multiple growing seasons in 2022 and 2023, where six different planting dates, viz., P1:June 15, P2:June 30, P3:July 15, P4:July 30, P5:August 15 and P6:August 30 and two fertilizer modules, FM1:Jeevamrit @ 30 ml plant−1 and FM2:NPK @ 30 g m−2 were systematically examined using a Randomized Block Design (factorial), replicated thrice.ResultsP6 planting resulted in early bud formation (44.03 days) and harvesting stage (90.78 days). Maximum plant height (79.44 cm), plant spread (34.04 cm), cut stem length (68.40 cm), flower diameter (7.83 cm), stem strength (19.38˚), vase life (14.90 days), flowering duration (24.08 days), available soil N (314 kg ha−1), available P (37 kg ha−1), available K (347 kg ha−1), bacterial count (124.87 × 107 cfu g−1 soil), actinomycetes count (60.72 × 102 cfu g−1 soil), fungal count (30.95 × 102 cfu g−1 soil), microbial biomass (48.79 µg g−1 soil), dehydrogenase enzyme (3.64 mg TPF h−1 g−1 soil) and phosphatase enzyme (23.79 mol PNP h−1 g−1 soil) was recorded in P1 planting. Among the fertilization module, minimum days to bud formation (74.94 days) and days to reach the harvesting stage (120.95 days) were recorded with the application of NPK @30 g m−2. However, maximum plant height (60.62 cm), plant spread (23.10 cm), number of cut stems m−2 (43.88), cut stem length (51.34 cm), flower diameter (6.92 cm), stem strength (21.24˚), flowering duration (21.75 days), available soil N (317 kg ha−1), available P (37 kg ha−1) and available K (349 kg ha−1) were also recorded with the application of NPK @300 kg ha−1. Maximum vase life (13.87 days), OC (1.13%), bacterial count (131.65 × 107 cfu g−1 soil), actinomycetes count (60.89 × 102 cfu g−1 soil), fungal count (31.11 × 102 cfu g−1 soil), microbial biomass (51.27 µg g−1 soil), dehydrogenase enzyme (3.77 mg TPF h−1 g−1 soil) and phosphatase enzyme (21.72 mol PNP h−1 g−1 soil) were observed with the application of Jeevamrit @ 30 ml plant−1.ConclusionEarly planting (P1) and inorganic fertilization (NPK @ 30 g m−2) resulted in improved yield and soil macronutrient content. The soil microbial population and enzymatic activity were improved with the jeevamrit application. This approach highlights the potential for improved yield and soil health in chrysanthemum cultivation, promoting a more eco-friendly and economically viable agricultural model.

Evaluating the Physical and Chemical Characteristics of Soil from Several Blocks in Mirzapur District, Uttar Pradesh, India

An evaluation of the physical-chemical characteristics of soil was conducted in 2023–24 in several blocks within the Mirzapur district of Uttar Pradesh. This study's main goals were to assess the physico-chemical characteristics of soil in several blocks in Uttar Pradesh's Mirzapur District in ord`er to ascertain the soil's macronutrient availability. Samples of soil were taken at three different depths: 0–15 cm, 15–30 cm, and 30-45 cm. Sandy loam was the textural class of soil. 1.17 mg m-3 to 1.44 mg m-3 is the bulk density. Density of Particles (2.22 - 2.48 mg m-3). Pore Space Percentage (42.78% to 48.00%). It was evident that the soil had good physical conditions, an alkaline pH range of 7.43 to 8.15, a good water holding capacity (39.92 to 45.76%), and and all crops could benefit from the electrical conductivity (0.21 to 0.34 dSm-1). Organic carbon content in these soils is modest (0.27 to 0.38%). All villages have a medium nitrogen range (250 kg ha-1 to 380 kg ha-1). All villages have a medium phosphorus level (22.53 kg ha-1 to 37.49 kg ha1). (148.43 kg ha-1 to 282.4 kg ha-1) of potassium. is high in one place and medium in eight others.

Transcriptional, metabolic, physiological and developmental responses to nitrogen limitation in switchgrass (Panicum virgatum)

Sustainable switchgrass production (Panicum virgatum) as a bioenergy crop hinges partly on efficiently using soil macronutrients, especially nitrogen (N). In this study, switchgrass plants were grown under four different N conditions (6.0 mM KNO3, N-replete control; 2.0 mM, mild limitation; 0.6 mM, moderate limitation; and 0.2 mM, severe limitation). Subsequently, the physiological, metabolic, and transcriptomic responses of switchgrass roots and shoots to N limitation were determined.Moderate N limitation marked a tipping point for large changes in plant growth, root-to-shoot ratio, root system architecture (root surfaces and specific root length - SRL) and total nitrogen content. Integrating metabolic data and transcriptomic analyses revealed that N limitation reduced switchgrass photosynthetic capacity and carbon(C)-fixation activities. Our results indicate that switchgrass balances C-fixation with N-assimilation and N transport and recycles N-compounds by rerouting C-flux from glycolysis, the oxidative branch of the pentose phosphate pathway (OPPP), and from the tricarboxylic acid (TCA) cycle in an organ-specific manner. The energy and reduction power so generated, and C-skeletons appear to be directed towards N uptake, biosynthesis of energy storage compounds with high C/N ratio such as sucrose, non-N-containing lipids, and various branches of secondary metabolism. Taken together, the findings of this study provide new insights into how switchgrass plants adapt to N limitation.

A novel capacitively coupled contactless conductivity detection (C4D) microfluidic chip integrated 3D microelectrodes for on-site determination of soil nutrients

Soil macronutrient nutrients (N, P and K) are critical for crop growth and agricultural production. The rapid and quantitative determination of soil nutrient content is of great importance to guide precise fertilization. To address the difficulty of synchronizing the detection of soil N, P and K with high sensitivity, this paper proposes a novel C4D microfluidic device with integrated 3D microelectrodes, which achieves lower detection limits for nutrients detection. The chip consisted of a crossover-type electrophoretic microchannels system and a 3D microelectrodes C4D system. 3D microelectrodes were constructed through clever design with only a single photolithography process required. The proposed microfluidic chips were used to detect potassium, ammonium, nitrate, and phosphate, with experimental results of standard solutions showing low detection limits of 5.24 × 10−5 g/L, 2.81 × 10−5 g/L, 2.35 × 10−5 g/L and 2.38 × 10−5 g/L. The microfluidic chips showed good reproducibility with relative standard deviations of less than 5 %. Separation experiments were also performed on soil samples with a resolution of K+ and NH4+ greater than 1.1. Accuracy was evaluated by adding 10 mg/L and 50 mg/L standards to the soil samples and results showed that recoveries were maintained at 80–120 %. The microfluidic chip with integrated 3D microelectrodes proposed in this work would effectively address the need for on-site, rapid detection of soil nutrients.

Decoding seasonal changes: soil parameters and microbial communities in tropical dry deciduous forests.

In dry deciduous tropical forests, both seasons (winter and summer) offer habitats that are essential ecologically. How these seasonal changes affect soil properties and microbial communities is not yet fully understood. This study aimed to investigate the influence of seasonal fluctuations on soil characteristics and microbial populations. The soil moisture content dramatically increases in the summer. However, the soil pH only gradually shifts from acidic to slightly neutral. During the summer, electrical conductivity (EC) values range from 0.62 to 1.03 ds m-1, in contrast to their decline in the winter. The levels of soil macronutrients and micronutrients increase during the summer, as does the quantity of soil organic carbon (SOC). A two-way ANOVA analysis reveals limited impacts of seasonal fluctuations and specific geographic locations on the amounts of accessible nitrogen (N) and phosphorus (P). Moreover, dehydrogenase, nitrate reductase, and urease activities rise in the summer, while chitinase, protease, and acid phosphatase activities are more pronounced in the winter. The soil microbes were identified in both seasons through 16S rRNA and ITS (Internal Transcribed Spacer) gene sequencing. Results revealed Proteobacteria and Ascomycota as predominant bacterial and fungal phyla. However, Bacillus, Pseudomonas, and Burkholderia are dominant bacterial genera, and Aspergillus, Alternaria, and Trichoderma are dominant fungal genera in the forest soil samples. Dominant bacterial and fungal genera may play a role in essential ecosystem services such as soil health management and nutrient cycling. In both seasons, clear relationships exist between soil properties, including pH, moisture, iron (Fe), zinc (Zn), and microbial diversity. Enzymatic activities and microbial shift relate positively with soil parameters. This study highlights robust soil-microbial interactions that persist mainly in the top layers of tropical dry deciduous forests in the summer and winter seasons. It provides insights into the responses of soil-microbial communities to seasonal changes, advancing our understanding of ecosystem dynamics and biodiversity preservation.

Enriching soybean with two soil macronutrients through boosting root proliferation with Trichoderma viride

Enriching soybean with two soil macronutrients through boosting root proliferation with Trichoderma viride

Assessing agricultural potential and environmental sustainability through quantification of soil macronutrients

Assessing agricultural potential and environmental sustainability through quantification of soil macronutrients

Estimating Macronutrient Content of Paddy Soil Based on Near-Infrared Spectroscopy Technology Using Multiple Linear Regression

This study investigates the feasibility of employing near-infrared (NIR) spectroscopy with multiple linear regression (MLR) to estimate macronutrients in paddy soil compared with partial least squares (PLS) and principal component regression (PCR). Seventy-nine soil samples from West Java Province, Indonesia, are subject to conventional nutrient analysis and NIR spectroscopy (1000-2500 nm). The reflectance data undergoes various pretreatment techniques, and MLR models are calibrated using the forward method to achieve correlations exceeding 0.90. The best model calibrations are selected based on high correlation coefficients, determination coefficients, RPD, and low RMSE values. Meanwhile, the comparison of performance MLR is made with the PLS and PCR models. Results indicate that simple MLR models perform less than PLS for all nutrients, better than PCR for nitrogen, and below PCR for phosphorus and potassium. However, MLR reliably estimates soil nitrogen, phosphorus, and potassium content with ratio of performance to deviation (RPD) exceeding 2.0. This study demonstrates the potential of MLR for precise macronutrient estimation in paddy soil.

Spatial distribution of soil organic carbon and macronutrients in the deep soil across a chronosequence of tea agroforestry

Due to the rapid growth of the tea industry, the tea plantation area has steadily increased worldwide. In some regions, croplands and natural forests have been converted into tea plantations causing habitat fragmentation, reducing landscape connectivity and loss of biodiversity. On the other hand, ancient tea forests, rustic tea and tea agroforestry have been shown to support higher native biodiversity. However, there is a dearth of information on the potential of tea agroforestry systems (TAFS) to store soil organic carbon (SOC) and nutrients in deeper soil layers. For the first time, here we provide estimates of concentrations and stocks of SOC and available nitrogen (N), phosphorus (P) and potassium (K) in deep soils in a chronosequence of tea agroforestry and adjacent natural forest stands. The concentrations of SOC, available N, P and K steadily decreased with soil depth across all the chronosequence of TAFS and natural forest stands. The SOC stocks in the 0–500 cm soil depth varied from 174.5 Mg C/ha in 20 years old TAFS to 213.2 Mg C/ha in 60 years old TAFS and 311 Mg C/ha in natural forest stands. Based on the empirical data, we propose the following predictive models for estimation of deep SOC concentrations (Y): Y=0.55+14.67e-0.013D in TAFS and Y=1.04+31.53e-0.014D in natural forests. The highest available N stocks were found in 60 years old TAFS (181.7 kg ha−1) while the available P and K stocks were highest in the forest soils (22.42 kg ha−1 and 56.86 kg ha−1). As in SOC, clay content and the SOC: clay ratios declined with soil depth in all the land uses studied. The SOC: clay ratios indicated a good soil structure in the study sites. It is concluded that TAFS store 39–46 % the SOC stocks and over 50 % of the available N, P and K stocks in the 100–500 cm soil depths. The predictive models developed here could be used for estimating deep soil SOC stocks after validation with independent datasets.

Characterisation and Mapping of Soils in Major Coffee Growing Regions of Uganda

There is limited information on the soil nutrient status, site specific fertilizer and agronomic recommendations for coffee in Uganda hence limiting its production and productivity. Therefore, this study was undertaken to characterize the soils under coffee farms and provide fertilizer and land management recommendations for coffee farming in selected districts in Uganda. 717 soil samples were collected from 45 coffee growing districts that were purposively selected to represent the major coffee growing regions of Eastern, Northern, Western, Mid-west and West Nile. 35 districts were sampled from Robusta coffee growing areas and 10 districts from Arabica coffee growing areas. Parameters considered in the laboratory analysis included; pH, total organic carbon, total nitrogen, available P, exchangeable Calcium (Ca), Sodium (Na), Magnesium (Mg), and Potassium (K) and micro elements: Iron (Fe), Zinc (Zn), Copper (Cu), Manganese (Mn) and Boron, soil texture, bulk density and hydraulic conductivity. Soil nutrient levels distribution maps for Robusta and Arabica coffee growing regions were generated in ArcGIS for the entire country. The soil chemical and physical properties were subjected to analysis of variance using Genstat 14th edition. The soil mapping results showed that, the overall average soil macro-nutrients concentrations were significantly different across regions (p < 0.05) with Eastern having the highest levels of macro elements (CEC of 19.28 meq/100 g, Base Saturation (BS) of 43.40%, pH of 5.78, N of 0.20%, K of 1.64 meq/100 g, P of 107.68 mg/kg and OM of 3.31%) followed by Western region. No significant difference (p > 0.05) was recorded for the micronutrients across the different regions, except Zn (p < 0.05). This study showed that Phosphorus and Potassium were generally moderate to high levels in most of the regions sampled while Nitrogen and organic matter were moderate to low. Results from this study provide a general picture of the nutrient status across all coffee growing regions in Uganda and highlight the required modifications for increased production and productivity.

Assessment of Soil Fertility Status Using Soil Nutrient Index (SNI) With Special Reference to pH, SOC, Ec, N, P, K and S of Jabalpur Block in Jabalpur District, M.P, India

Soil nutrients are the key factors that influence crop productivity and plant growth. The present study gives information about the soil nutrient status of Jabalpur Block in Jabalpur District, Madhya Pradesh. 30 soil samples were collected from the field at a depth of 0-15 cm. Soil samples were collected using grid sampling techniques and the location of soil sampling sites was determined using GPS. Soil samples were further analysed to determine the soil nutrient index (SNI) using the soil chemical properties such as pH, OC, EC, and soil macronutrients i.e. N, P, K and Sulphur (S), etc. The nutrients are further categorized as low, medium, and high as per the soil nutrient index (NI). The spatial variation of soil nutrient parameters was represented by using the interpolation method in the ArcGIS environment. Based on the fertility measurement, the pH of the Jabalpur block ranges from 7.1 to 7.9, which is indicative of Neutral to alkaline nature, and organic carbon content ranges from 0.7 % to 1.78 %, indicating that the amount of SOC is medium to high along with available N ranging from (90kg/ha to 320kg/ha), available P with (20.16kg/ha to 47.04kg/ha) and available K of (29.12 kg/ha to 118kg/ha). The fertility status of Jabalpur block in this study is primarily constrained by soil pH, as well as N, P, K and S. Therefore, to enrich the soil fertility status of Jabalpur block, proper nutrient management practices, application of different organic manures, suitable cropping pattern and sustainable use of agricultural land are very essential.

Effect of Mycorrhizae, Azotobacter and Vermicompost Tea on Nitrogen, Phosphorus, and Potassium (NPK) Concentrations in Soil and Cucumber Plants (Cucumis sativus)

An experiment was carried out inside a greenhouse (sandy loam soil) at the College of Agriculture / University of Anbar to investigate the effect of Vesicular-Arbuscular Mycorrhizaee, Azotobacterchroococcum and Vermicompost tea on the concentration of NPK macronutrients in soil and cucumber plants. A factorial experiment consisting of two factors was designed for this purpose: the first factor is a combination of mycorrhizae (M) with 35 g plant−1, azotobacter (A) bacteria 16 ml plant−1 with a microbial density of 2.2 x 109 cfu ml−1 and phosphate Rock (R) with 40 g plant−1, added with half of the mineral fertilizer recommendation each individually with its interactions, the second factor is the addition of three concentrations (0, 5, 10%) of vermicompost tea sprayed on the plant. The experiment was designed with a completely randomized block design (RCBD) with three replications. Cucumber seeds were planted on 15/9/2022 and service operations were conducted for the crop and the experiment continued until 15/12/2022. The result showed highest percentage of phosphorus in the leaves (PIL) (0.40%), the highest potassium in the leaves (KL) (1.95%), followed by the treatment of adding mycorrhizae with phosphate Rock and half the mineral recommendation (MR) with vermicompost tea at a concentration of 10%, then the treatment of adding mycorrhizae with azotobacter bacteria with half the mineral recommendation (AR) with 10% of vermicompost tea, then the treatment of mycorrhizae with phosphate Rock plus half the fertilizer recommendation (MR) with 10% of vermicompost tea, then the treatment of mycorrhizae with azotobacter plus half of the fertilizer recommendation (MA) with 10% of vermicombe tea.

Deep residual network for soil nutrient assessment using optical sensors

AbstractBackgroundFarmers need information regarding soil fertility at every location of their fields to attain a higher level of precision in nutrient management. Nonetheless, the acquisition and processing of soil samples are labor‐intensive and time‐utilizing, and the related cost remains high‐priced to farmers. Artificial intelligence is the most speedily growing area combined into approximately all aspects of human life. Soil macronutrients like nitrogen (N), phosphorous (P), and potassium (K) have a significant role in precision agriculture. There is a huge need for powerful and rapid measurement systems to measure accurately the macronutrients in the soil for optimal crop productivity, especially in site‐specific crop management system, where the application of fertilizer can be regulated spatially with respect to crop demand. Nevertheless, it can present a research direction to design an advanced scheme in order to predict the properties of soil. A portable sensor device is a basic need of an agriculture system for the accurate and rapid monitoring of soil macronutrients.AimIn this research, the soil nutrients identified from the collected soil samples using optical sensors are evaluated for their accuracy using a deep learning approach.MethodsA deep residual network is exploited for the soil nutrient prediction after augmenting the gathered soil data. Finally, various performance evaluation measures, like mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE), are calculated to detect how accurately the sensor predicted the soil nutrients.ResultsFrom the experimental analysis, it is stated that the proposed model attained low MSE value of 4.59 e−09, the low RMSE value of 6.78 e−05, and the low MAE value of 4.66 e−05 for N prediction. Likewise, the proposed model attained the least MSE value of 1.41 e−05, the least RMSE value of 0.0003, and the least MAE value of 0.0001 for P prediction.ConclusionFinally, for K prediction, the proposed model achieved the least MSE value of 1.54 e−06, least RMSE value of 1.24 e−03, and the least MAE value of 1.38 e−05.

Site History’s Role in Urban Agriculture: A Case Study in Kisumu, Kenya, and Ouagadougou, Burkina Faso

Urban agriculture (UA) is a widespread practice often considered low-profit, taking place on marginal lands. This is supported by the lack of quantitative data on UA’s contributions to food security and employment, yet contradicted by prevalence and high participation rates. This case study of six UA sites in Kisumu, Kenya and Ouagadougou, Burkina Faso explores the relationship between prior land use and current management and soil quality. A soil survey is performed determining the soil macronutrient and soil mineral composition. Agricultural management, ownership, and prior land use are investigated through interviews, satellite imagery, and historic publications. Results show three UA sites predating surrounding urban development, and data on soil nutrient content show that sites likely were chosen for their soil. The three younger sites are smaller and less embedded in the local economy, but soil analysis shows medium-rich to rich agricultural soils. We conclude that one cannot assume that UA is practiced on marginalized soils. Consequently, both value attribution to and the sustainable agricultural management of UA soils must be based on their characteristics, such as mineralogy and nutrient status, to prevent valuable soil resources from being lost. Through this, the more accurate value attribution of UA can be achieved, lending weight to the value attributed to UA by local communities.