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On-the-go soil sensors have emerged as promising tools for real-time, high-resolution soil nutrient monitoring in precision agriculture. This review provides a comprehensive overview of the current state-of-the-art in on-the-go soil sensor technology, discussing the potential benefits, limitations, and applications of various sensor types, including optical sensors (Vis-NIR, MIR, ATR spectroscopy) and electrochemical sensors (ISEs, ISFETs). The integration of these sensors with positioning systems (GPS) enables the generation of detailed soil nutrient maps, which can guide site-specific management practices and optimize fertilizer application rates. However, factors such as soil moisture, texture, and heterogeneity can affect sensor performance, necessitating robust calibration models and standardized protocols. Future perspectives highlight the need for multi-sensor systems, incorporation into IoT networks for smart farming, and enhancing affordability and adoptability of on-the-go sensor technologies to promote widespread adoption in precision agriculture. [ABSTRACT FROM AUTHOR]

Sensor-based decision tools provide a quick assessment of nutritional and physiological health status of crop, thereby enhancing the crop productivity. Therefore, a 2-year field study was undertaken with precision nutrient and irrigation management under system of crop intensification (SCI) to understand the applicability of sensor-based decision tools in improving the physiological performance, water productivity, and seed yield of soybean crop. The experiment consisted of three irrigation regimes [I1: standard flood irrigation at 50% depletion of available soil moisture (DASM) (FI), I2: sprinkler irrigation at 80% ETC (crop evapo-transpiration) (Spr 80% ETC), and I3: sprinkler irrigation at 60% ETC (Spr 60% ETC)] assigned in main plots, with five precision nutrient management (PNM) practices{PNM1-[SCI protocol], PNM2-[RDF, recommended dose of fertilizer: basal dose incorporated (50% N, full dose of P and K)], PNM3- [RDF: basal dose point placement (BDP) (50% N, full dose of P and K)], PNM4-[75% RDF: BDP (50% N, full dose of P and K)] and PNM5-[50% RDF: BDP (50% N, full P and K)]} assigned in sub-plots using a split-plot design with three replications. The remaining 50% N was top-dressed through SPAD assistance for all the PNM practices. Results showed that the adoption of Spr 80% ETC resulted in an increment of 25.6%, 17.6%, 35.4%, and 17.5% in net-photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), and intercellular CO2 concentration (Ci), respectively, over FI. Among PNM plots, adoption of PNM3 resulted in a significant (p=0.05) improvement in photosynthetic characters like Pn (15.69 µ mol CO2 m-2 s-1), Tr (7.03 m mol H2Om-2 s-1), Gs (0.175 µmol CO2 mol-1 year-1), and Ci (271.7 mol H2O m2 s-1). Enhancement in SPAD (27% and 30%) and normalized difference vegetation index (NDVI) (42% and 52%) values were observed with nitrogen (N) top dressing through SPAD-guided nutrient management, helped enhance crop growth indices, coupled with better dry matter partitioning and interception of sunlight. Canopy temperature depression (CTD) in soybean reduced by 3.09-4.66°C due to adoption of sprinkler irrigation. Likewise, Spr 60% ETc recorded highest irrigation water productivity (1.08 kg ha-1 m-3). However, economic water productivity (27.5 INR ha-1 m-3) and water-use efficiency (7.6 kg ha-1 mm-1 day-1) of soybean got enhanced under Spr 80% ETc over conventional cultivation. Multiple correlation and PCA showed a positive correlation between physiological, growth, and yield parameters of soybean. Concurrently, the adoption of Spr 80% ETC with PNM3 recorded significantly higher grain yield (2.63 t ha-1) and biological yield (8.37 t ha-1) over other combinations. Thus, the performance of SCI protocols under sprinkler irrigation was found to be superior over conventional practices. Hence, integrating SCI with sensor-based precision nutrient and irrigation management could be a viable option for enhancing the crop productivity and enhance the resource-use efficiency in soybean under similar agroecological regions. [ABSTRACT FROM AUTHOR]

A timing and precise diagnosis of crop nutrient status is essential for optimizing management practices that promote environmentally friendly and enhanced crop yields. Although plant tissue analysis has conventionally been employed to evaluate the nutritional status of crops, this method cannot capture the spatial variability of crop nutrients. In contrast, satellite-based remote sensing can monitor the nutrient status of crops across expansive areas. This study explored the capability of multi-source satellite images (PlanetScope-4: 3 m, 4 bands; PlanetScope-8: 3 m, 8 bands; Sentinel-2: 10–60 m, 13 bands; PRISMA: 30 m, 239 bands) in mapping 12 foliar nutrients in cranberries. Three machine learning approaches, including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), were used to relate foliar nutrients to different types of satellite-derived features (SR: surface reflectance; VI: vegetation indices; TF: texture features) or their combinations (SR+VI, VI+TF and SR+VI+TF). Model performance was compared across different foliar nutrients, modelling approaches and combinations of model input features using R2 (the coefficient of determination) and RRMSE (relative root mean square error, = root mean square error/nutrient range × 100 %). Input features that were important to foliar nutrient modelling were identified. The model performance difference among nutrients was consistent between Planet-4 and Sentinel-2, as well as between Planet-8 and PRISMA. In the Planet-4 and Sentinel-2 derived models, N was best predicted (average R2 = 0.77, average RRMSE=15 %), followed by macronutrients S (0.60–0.63, 11 %), Mg (0.58–0.65, 10–11 %), Ca (0.49–0.51, 9 %), Na (0.69, 22 %), P (0.49, 9 %) and K (0.20, 8 %), and then by all micronutrients(i.e., Fe, Mn, B, Cu and Zn: R2 = 0.04–0.61; RRMSE=16–28 %). In the Planet-8 and PRISMA derived models, macronutrients (i.e., N, P, K, Mg, Ca, S and Na) had lower R2 and RRMSE (R2 = 0.06–0.59; RRMSE=7–57 %) than micronutrients (i.e., Fe, Mn, B, Cu and Zn: R2 = 0.18–0.60; RRMSE=19–66 %). The successful retrieval of foliar nutrients from satellite imagery was influenced by many factors, including the intercorrelation between nutrients and model input features, the data availability at critical growth stages, and satellite images characteristics (e.g., spatial and spectral resolutions). Except for foliar nitrogen, foliar nutrients typically do not exhibit distinct absorption features associated with C, H, N, or O molecular bonds in the 400–2500 nm range. Our results indicate that their successful retrieval can be primarily attributed to the association between foliar nutrients and other leaf components (e.g., pigments, water, and dry matter) that do display spectral features within this range. Our study demonstrated the potential of integrating multi-source satellite data for precise nutrient monitoring over large scales.

Accurate and near real-time volumetric soil water and volumetric ion content (VIC) measurements can both inform precise irrigation scheduling and aid in fertilizer management applications in cropping systems. To assist in themonitoring of these measurements, capacitance-based soil moisture probes are used in agricultural best management practice (BMP) programs. However, the ability of these sensors to detect nutrients in the soil sourced from fertilizers is not well understood. The objective of this study was to evaluate the sensitivity of a capacitance-based soil moisture probe in detecting Nitrogen (N), Phosphorous (P), and Potassium (K) movement in the soil. To achieve this, a laboratory-based setup was established using pure sand soil cores. Raw soil moisture and VIC probe readings from the cores were contrasted across multiple N, P, and K rates. The N treatments applied were rates of 0, 112, 168, and 224 kg/ha; for P, were 0, 3.76, and 37.6 kg/ha, and for K were 0, 1.02, 1.53, and 2.04 kg/ha. Each nutrient was evaluated separately using a randomized complete block design experiment with three replications for N and K, and 5 replications for P. The impact of each nutrient rate on the sensitivity of VIC readings was determined by evaluating differences in three points of the time series, including the observed maximum point, inflection point, and convergence value as well as the time of occurrence of those points over a 24-hour period. These points were assessed at depths 5, 15, 25, 35, 45, and 55 cm. The findings of this study highlight the capacitance-based soil moisture probes' responsiveness to changes in all K rates at most depths. However, its sensitivity to changes in N and P rates is comparatively lower. The results obtained in this study can be used to develop fertilizer management protocols that utilize K movement as the baseline to indirectly assess N and P, while helping to inform those who currently use the probe which nutrients the probe may be detecting. The probes' readings could be incorporated into decision support systems for irrigation and nutrient management and improve control systems for precision water and nutrient management. [ABSTRACT FROM AUTHOR]

Sensor-based decision tools provide a quick assessment of nutritional and physiological health status of crop, thereby enhancing the crop productivity. Therefore, a 2-year field study was undertaken with precision nutrient and irrigation management under system of crop intensification (SCI) to understand the applicability of sensor-based decision tools in improving the physiological performance, water productivity, and seed yield of soybean crop. The experiment consisted of three irrigation regimes [I1: standard flood irrigation at 50% depletion of available soil moisture (DASM) (FI), I2: sprinkler irrigation at 80% ETC (crop evapo-transpiration) (Spr 80% ETC), and I3: sprinkler irrigation at 60% ETC (Spr 60% ETC)] assigned in main plots, with five precision nutrient management (PNM) practices{PNM1-[SCI protocol], PNM2-[RDF, recommended dose of fertilizer: basal dose incorporated (50% N, full dose of P and K)], PNM3- [RDF: basal dose point placement (BDP) (50% N, full dose of P and K)], PNM4-[75% RDF: BDP (50% N, full dose of P and K)] and PNM5-[50% RDF: BDP (50% N, full P and K)]} assigned in sub-plots using a split-plot design with three replications. The remaining 50% N was top-dressed through SPAD assistance for all the PNM practices. Results showed that the adoption of Spr 80% ETC resulted in an increment of 25.6%, 17.6%, 35.4%, and 17.5% in net-photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), and intercellular CO2 concentration (Ci), respectively, over FI. Among PNM plots, adoption of PNM3 resulted in a significant (p=0.05) improvement in photosynthetic characters like Pn (15.69 µ mol CO2 m-2 s-1), Tr (7.03 m mol H2Om-2 s-1), Gs (0.175 µmol CO2 mol-1 year-1), and Ci (271.7 mol H2O m² s-1). Enhancement in SPAD (27% and 30%) and normalized difference vegetation index (NDVI) (42% and 52%) values were observed with nitrogen (N) top dressing through SPAD-guided nutrient management, helped enhance crop growth indices, coupled with better dry matter partitioning and interception of sunlight. Canopy temperature depression (CTD) in soybean reduced by 3.09-4.66°C due to adoption of sprinkler irrigation. Likewise, Spr 60% ETc recorded highest irrigation water productivity (1.08 kg ha-1 m-3). However, economic water productivity (27.5 INR ha-1 m-3) and water-use efficiency (7.6 kg ha-1 mm-1 day-1) of soybean got enhanced under Spr 80% ETc over conventional cultivation. Multiple correlation and PCA showed a positive correlation between physiological, growth, and yield parameters of soybean. Concurrently, the adoption of Spr 80% ETC with PNM3 recorded significantly higher grain yield (2.63 t ha-1) and biological yield (8.37 t ha-1) over other combinations. Thus, the performance of SCI protocols under sprinkler irrigation was found to be superior over conventional practices. Hence, integrating SCI with sensor-based precision nutrient and irrigation management could be a viable option for enhancing the crop productivity and enhance the resource-use efficiency in soybean under similar agroecological regions. [ABSTRACT FROM AUTHOR]

Accurate and near real-time volumetric soil water and volumetric ion content (VIC) measurements can both inform precise irrigation scheduling and aid in fertilizer management applications in cropping systems. To assist in the monitoring of these measurements, capacitance-based soil moisture probes are used in agricultural best management practice (BMP) programs. However, the ability of these sensors to detect nutrients in the soil sourced from fertilizers is not well understood. The objective of this study was to evaluate the sensitivity of a capacitance-based soil moisture probe in detecting Nitrogen (N), Phosphorous (P), and Potassium (K) movement in the soil. To achieve this, a laboratory-based setup was established using pure sand soil cores. Raw soil moisture and VIC probe readings from the cores were contrasted across multiple N, P, and K rates. The N treatments applied were rates of 0, 112, 168, and 224 kg/ha; for P, were 0, 3.76, and 37.6 kg/ha, and for K were 0, 1.02, 1.53, and 2.04 kg/ha. Each nutrient was evaluated separately using a randomized complete block design experiment with three replications for N and K, and 5 replications for P. The impact of each nutrient rate on the sensitivity of VIC readings was determined by evaluating differences in three points of the time series, including the observed maximum point, inflection point, and convergence value as well as the time of occurrence of those points over a 24-hour period. These points were assessed at depths 5, 15, 25, 35, 45, and 55 cm. The findings of this study highlight the capacitance-based soil moisture probes’ responsiveness to changes in all K rates at most depths. However, its sensitivity to changes in N and P rates is comparatively lower. The results obtained in this study can be used to develop fertilizer management protocols that utilize K movement as the baseline to indirectly assess N and P, while helping to inform those who currently use the probe which nutrients the probe may be detecting. The probes’ readings could be incorporated into decision support systems for irrigation and nutrient management and improve control systems for precision water and nutrient management.

Sensor-based decision tools provide a quick assessment of nutritional and physiological health status of crop, thereby enhancing the crop productivity. Therefore, a 2-year field study was undertaken with precision nutrient and irrigation management under system of crop intensification (SCI) to understand the applicability of sensor-based decision tools in improving the physiological performance, water productivity, and seed yield of soybean crop. The experiment consisted of three irrigation regimes [I1: standard flood irrigation at 50% depletion of available soil moisture (DASM) (FI), I2: sprinkler irrigation at 80% ETC (crop evapo-transpiration) (Spr 80% ETC), and I3: sprinkler irrigation at 60% ETC (Spr 60% ETC)] assigned in main plots, with five precision nutrient management (PNM) practices{PNM1-[SCI protocol], PNM2-[RDF, recommended dose of fertilizer: basal dose incorporated (50% N, full dose of P and K)], PNM3-[RDF: basal dose point placement (BDP) (50% N, full dose of P and K)], PNM4-[75% RDF: BDP (50% N, full dose of P and K)] and PNM5-[50% RDF: BDP (50% N, full P and K)]} assigned in sub-plots using a split-plot design with three replications. The remaining 50% N was top-dressed through SPAD assistance for all the PNM practices. Results showed that the adoption of Spr 80% ETC resulted in an increment of 25.6%, 17.6%, 35.4%, and 17.5% in net-photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), and intercellular CO2 concentration (Ci), respectively, over FI. Among PNM plots, adoption of PNM3 resulted in a significant (p=0.05) improvement in photosynthetic characters like Pn (15.69 µ mol CO2 m−2 s−1), Tr (7.03 m mol H2O m−2 s−1), Gs (0.175 µmol CO2 mol−1 year−1), and Ci (271.7 mol H2O m2 s−1). Enhancement in SPAD (27% and 30%) and normalized difference vegetation index (NDVI) (42% and 52%) values were observed with nitrogen (N) top dressing through SPAD-guided nutrient management, helped enhance crop growth indices, coupled with better dry matter partitioning and interception of sunlight. Canopy temperature depression (CTD) in soybean reduced by 3.09–4.66°C due to adoption of sprinkler irrigation. Likewise, Spr 60% ETc recorded highest irrigation water productivity (1.08 kg ha−1 m−3). However, economic water productivity (27.5 INR ha−1 m−3) and water-use efficiency (7.6 kg ha−1 mm−1 day−1) of soybean got enhanced under Spr 80% ETc over conventional cultivation. Multiple correlation and PCA showed a positive correlation between physiological, growth, and yield parameters of soybean. Concurrently, the adoption of Spr 80% ETC with PNM3 recorded significantly higher grain yield (2.63 t ha−1) and biological yield (8.37 t ha−1) over other combinations. Thus, the performance of SCI protocols under sprinkler irrigation was found to be superior over conventional practices. Hence, integrating SCI with sensor-based precision nutrient and irrigation management could be a viable option for enhancing the crop productivity and enhance the resource-use efficiency in soybean under similar agro-ecological regions.

This document is a corrigendum for an article titled "Sensor-based precision nutrient and irrigation management enhances the physiological performance, water productivity, and yield of soybean under system of crop intensification." The corrigendum addresses errors in the original article, specifically in the discussion section where incorrect tables and references were cited. The authors apologize for the errors and state that they do not affect the scientific conclusions of the article. The original article has been updated. [Extracted from the article]

Fertilizer use and genetic improvement of cereal crops contributed to increased yields and greater food security in the last six decades. For rice, however, fertilizer use has outpaced improvement in yield. Excess application of nutrients beyond crop needs, especially nitrogen (N), is associated with losses to the environment. Environmental pollution can be mitigated by addressing fertilizer overuse, improving N use efficiency, while maintaining or improving rice productivity and farmers' income. A promising approach is the site-specific nutrient management (SSNM), developed in the 1990s to optimize supply to meet demand of nutrients, initially for rice, but now extended to other crops. The SSNM approach has been further refined with the development of digital decision support tools such as Rice Crop Manager, Nutrient Expert, and RiceAdvice. This enables more farmers to benefit from SSNM recommendations. In this mini-review, we show how SSNM can foster sustainability in rice production systems through improved rice yields, profit, and N use efficiency while reducing N losses. Farmer adoption of SSNM, however, remains low. National policies and incentives, financial investments, and strengthened extension systems are needed to enhance scaling of SSNM-based decision support tools.

An agronomic field experiment on "Precision nutrient management in maize (Zea mays L.) for higher productivity and profitability through Decision Support Tools" was conducted at ICAR-IARI New Delhi, India during kharif 2018. The objective was to enhance seed yield and economics of maize crop trough precision nutrient management. The growth (plant height, dry matter, leaf area, leaf area index), and yield attributes and seed yield were improved under precision nutrient management practices of NE+GS7T, NE6T, NE7T, and NE+GS 6T. The maximum LAR was observed with NE+GS7T at 60 DAS. Maximum cob length was 15.92 cm under NE7T. Plant height was maximum under NE7T (192.5 cm) and NE+GS7T (191.3 cm), similarly maximum dry matter accumulation was in NE7T, which was >22.0% over state recommendation at harvesting stage of maize crop. Grain yield, total biomass of maize was also the highest under NE7T nutrient management (6.2 t/ha). Maximum net return was in NE7T (` 75194/ ha) and least were in N omission (` 12992 to 20451/ha).

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

Abstract: This article puts forward a model of the role of phosphorus in crop production, soil phosphorus dynamics and phosphorus loading that integrates the salient economic and ecological features of phosphorus management, with grain production in southern Finland as an application. The model accounts for the links between phosphorus fertilization, crop yield, accumulation of soil phosphorus reserves, and phosphorus loading into waterways. It can be used to guide precision phosphorus management as a means to mitigate agricultural phosphorus loading. Erosion control is considered as an additional measure to reduce phosphorus loading through soil loss. A dynamic programming approach and numerical solution method are used to analyze the intertemporally optimal combination of fertilization and erosion control and the associated soil phosphorus development. The optimal fertilizer application rate changes markedly over time in response to changes in the soil phosphorus level. Erosion control was found to increase welfare only on land that is highly susceptible to erosion. [Copyright &y& Elsevier]

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant's nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients. [ABSTRACT FROM AUTHOR]