Search

Cereals are essential for global food security and serve as a primary source of dietary energy for 60% of the world's population. Maize, among the prominent cereals, stands out as a highly adaptable crop with versatility across diverse agro-climatic zones, fulfilling demands for food, feed, and fodder worldwide. While progressive farmers often adhere to conventional fertilizer recommendations set by authorities, this practice can sometimes result in suboptimal productivity with a negative impact soil health over time. In this context, the Nutrient Expert (NE) can be considered an efficient decision support system for field-specific recommendations for precision nutrient management in maize. Plant density is a vital agronomic trait determining the grain yield of maize. Considering these, a field study was conducted for two consecutive years at the Post Graduate Research Farm of Centurion University of Technology and Management, Paralakhemundi, Odisha, India (18.8° N latitude, 84.8° E longitude) during the rabi season of 2022-23 and 2023-24. the present research was conducted by adopting a split-plot design which comprised three main plot treatments with different levels of plant population (P1: 66666 plants/ha; P2: 83333 plants/ha; P3: 111111 plants/ha) and four sub-plot treatments consisting of nutrient management treatments (T1: 100 % RDF, T2: Nutrient Expert-based recommendation (NE) for a target yield of 6 t/ha, T3: NE for a target yield of 8 t/ha and T4: NE for a target yield of 10 t/ha). The experimental soil was sandy clay loam in texture with slightly acidic pH and low in available nitrogen and organic carbon, and medium in phosphorous and potassium. The results of the experiment revealed a significant difference among the growth parameters, yield attributing characters, yield and nutrient uptake of maize concerning plant population and nutrient levels. The study concluded that applying primary nutrients through NE @ 10 t/ha along with a plant population of 1,11,111 plants/ha could be chosen as the most suitable agronomic practice for improving growth and productivity in irrigated rabi maize under Odisha conditions. [ABSTRACT FROM AUTHOR]

Precision nutrient management is a modern approach for optimizing nutrient supply in fruit crops, ensuring that plants receive the actual amount of essential elements at the right time and place. Traditional nutrient management methods often undergo deficiencies, leading to over-fertilization, uneven distribution of nutrients, environmental pollution, and economic inefficiency. These review paper challenges can be addressed by providing real-time data of soil conditions, plant health and nutrient levels. This strategy depends on advanced technologies such as remote sensing, variable rate technology, fertigation, slow/control release fertilizer, and organic amendments to weave nutrient application to the specific needs of each crop and individual plant within a field. It is emphasized by its potential for plant growth and development, increased crop yield, optimized resource utilization and mitigated environmental concerns. By fine-tuning nutrient application, farmers can achieve better economic returns while promoting sustainable agriculture. Precision nutrient management for fruit crops is characterized by a scarcity of studies exploring the application of advanced technologies and data-driven approaches. There is a need for more in-depth investigation to develop and validate precision nutrient management strategies tailored to the unique requirements of different crops. Closing this research gap will contribute to sustainable and optimized fruit crop production. In conclusion, precision nutrient management represents a paradigm shift in agricultural practices, offering a more sustainable and efficient approach to nutrient application in fruit crops. By untried advanced technologies and data-driven insights, farmers can optimize their resource use, enhance crop performance, and contribute to the long-term sustainability of agriculture. [ABSTRACT FROM AUTHOR]

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.

[Display omitted] • Adequate variability in nutrient and spectral data is requisite for spectroscopy studies. • Vegetation indices and solo multivariate models poorly predicts cashew leaf nutrients. • The PLSR combined machine learning model predicts cashew leaf nutrients significantly. Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards. [ABSTRACT FROM AUTHOR]

Soil nutrients vary greatly and information regarding their spatial-temporal variability is critical for achieving site-specific nutrient management. However, the problem is that the high cost of soil sampling and laboratory analysis limits the number of samples current research budgets can afford. Besides, large sampling intervals sometimes fail to capture the variability of typical soil properties. Consequently, determining a reasonable yet feasible sampling interval to characterize the spatial variation of soil properties is greatly needed to delineate soil nutrient management zones (MZs). For this purpose, this study used four sampling intervals, 25 m × 25 m, 50 m × 50 m, 75 m × 75 m, and 100 m × 100 m by resampling the raw data acquired from seven adjacent fields located in Quzhou County to estimate the optimal design of soil sampling. Five soil properties (pH, electrical conductivity, extractable phosphorus, extractable potassium, and soil organic matter) were selected to delineate MZs using principal component analysis and fuzzy c-means clustering algorithm. The results indicated that indigenous P and K varied significantly, with the coefficients of variation of 34.33% and 24.01 %, respectively. The findings suggested that the optimal soil sampling interval was 50 m to accurately delineate MZs in medium- and small-scale farmlands. Besides, findings also demonstrated that site-specific P and K management could save approximately 21 kg P ha−1 and 30 kg K ha−1 as compared to conventional farming practices while ensuring target yield. The results can help make precision P and K management decisions in medium- and small-scale farming systems. [ABSTRACT FROM AUTHOR]

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).

After two decades of availability of grain yield-mapping technology, long-term trends in field-scale profitability for precision agriculture (PA) systems and conservation practices can now be assessed. Field-scale profitability of a conventional or 'business-as-usual' system with an annual corn (Zea mays L.)-soybean (Glycine max [L.]) rotation and annual tillage was assessed for 11 years on a 36 ha field in central Missouri during 1993 to 2003. Following this, a 'precision agriculture system' (PAS) with conservation practices was implemented for the next 11 years to address production, profit and environmental concerns. The PAS was multifaceted and temporally dynamic. It included no-till, cover crops, crop rotation changes, site-specific N and variable-rate or zonal P, K and lime. Following a recent evaluation of differences in yield and yield variability, this research compared profitability of the two systems. Results indicated that PAS sustained profits in the majority (97%) of the field without subsidies for cover crops or payments for enhanced environmental protection. Profit was only lower with PAS in a drainage channel where no-till sometimes hindered soybean stands and wet soils caused wheat (Triticum aestivum L.) disease. Although profit gains were not realized after 11 years of PA and conservation practices, this system sustained profits. These results should help growers gain confidence that PA and conservation practices will be successful. [ABSTRACT FROM AUTHOR]

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.

Till date, studies using canopy hyperspectral data to monitor crop nutrient status have focused mainly on biomass, water and nitrogen (N) prediction, and only a few have attempted to monitor phosphorus (P). This study aimed to evaluate the potential of the canopy raw spectra (R) in combination with a partial least square (PLS) regression model for estimating the leaf N and P concentration (LNC and LPC), compared to the potential of other hyperspectral transformation techniques such as log-transformed spectra (Log(1/R)), the continuum removal (CR) method and first derivative reflectance (FDR) for winter oilseed rape. Field experiments were conducted over three consecutive growing seasons (2013–2016) at different sites (Wuxue, Wuhan and Shayang) in Hubei, China, using different N and P application rates, planting patterns, cultivars and ecological sites. Data from the conventionally managed fields of 25 farmers in 2015–2016 were also collected to test the transferability of the established optimal monitoring model for LNC and LPC prediction. Canopy hyperspectral reflectance data were acquired over a wavelength range from 400 to1300 nm (the visible and near-infrared region, VNIR), and quantitative correlations between LNC and LPC and their spectra were determined. The results showed that the FDR-PLS model yielded the highest retrieval accuracy for LNC and LPC predictions. The coefficient of determination of the validation dataset (r 2 val ) between the observations and predictions was 0.89 for LNC and 0.82 for LPC, with a relative percent deviation (RPD val ) of 2.41 and 2.22, respectively. The variable importance in projection (VIP) values of the FDR-PLS model with full spectral range were applied to identify the effective wavelengths and to decrease the high dimensionality of the canopy hyperspectral reflectance dataset. Seven wavelengths centred at 445, 556, 657, 764, 985, 1082, and 1194 nm and six wavelengths at 755, 832, 891, 999, 1196, and 1267 nm were identified as effective wavelengths for predicting the LNC and LPC values. The newly-developed FDR-PLS models for LNC (r 2 val = 0.85, RPD val = 2.10) and LPC (r 2 val = 0.78, RPD val = 1.94) provided accurate estimations based on field experiment validations using the effective wavelengths. The validation in the farmers’ fields also indicated an excellent accuracy between the observed and predicted values for LNC (r 2 val = 0.82, RPD val = 2.09) and LPC (r 2 val = 0.75, RPD val = 2.01). The overall results demonstrated the applicability and feasibility of the FDR-PLS model for estimating the N and P status of winter oilseed rape using in situ canopy hyperspectral reflectance data. [ABSTRACT FROM AUTHOR]

Research is lacking on the long-term impacts of field-scale precision agriculture practices on grain production. Following more than a decade (1993-2003) of yield and soil mapping and water quality assessment, a multi-faceted, 'precision agriculture system' (PAS) was implemented from 2004 to 2014 on a 36-ha field in central Missouri. The PAS targeted management practices that address crop production and environmental issues. It included no-till, cover crops, growing winter wheat ( Triticum aestivum L.) instead of corn ( Zea mays L.) for field areas where corn was not profitable, site-specific N for wheat and corn using canopy reflectance sensing, variable-rate P, K and lime using intensively grid-sampled data, and targeting of herbicides based on weed pressure. The PAS assessment was accomplished by comparing it to the previous decade of conventional, whole-field corn-soybean ( Glycine max L.) mulch-tillage management. In the northern part of the field and compared to pre-PAS corn, relative grain yield of wheat in PAS was greatly improved and temporal yield variation was reduced on shallow topsoil, but relative grain yield was reduced on deep soil in the drainage channel. In the southern part of the field where corn remained in production, PAS did not lead to increased yield, but temporal yield variation was reduced. Across the whole field, soybean yield and temporal yield variation were only marginally influenced by PAS. Spatial yield variation of all three crops was not altered by PAS. Therefore, the greatest production advantage of a decade of precision agriculture was reduced temporal yield variation, which leads to greater yield stability and resilience to changing climate. [ABSTRACT FROM AUTHOR]

In the rainfed mid-hill region of Nepal, most fields receive 2–3 t ha −1 of organic compost application every year. Despite efficient recovery and use of organics in the mixed crop-animal systems that predominant in the mid-hills, depleted soil fertility is widely understood to be a significant constraint to crop productivity, with most farmers achieving maize grain yields below 2 t ha −1 . Increased use of fertilizer may arrest and even reverse long-term soil quality degradation, but few farmers in the mid-hills use them at present and existing recommendations are insufficiently responsive to site, varietal, and management factors that influence the productivity and profitability of increased fertilizer use. Moreover, policy makers and development practitioners often hold the perception that returns to fertilizer use in the mid-hills are too low to merit investment. In this study, on-farm experiments were conducted at 16 sites in the Palpa district, Nepal to assess the responsiveness of a maize hybrid (DKC 9081) and an ‘improved’ open-pollinated maize variety (‘OPV’, Manakamana-3) to four nitrogen (N) rates, i.e., 0, 60, 120 and 180 kg ha −1 , with each N rate response evaluated at 30:30 and 60:60 kg ha −1 rates of phosphorus (P 2 O 5 ) and potassium (K 2 O), respectively. With sound agronomy and high rates of fertilizer (180:60:60 kg N:P 2 O 5 :K 2 O ha −1 ), grain yields observed in the field experiments exceeded 8 t ha −1 with hybrids and 6 t ha −1 with OPV. Yield levels were lower for OPV than hybrid at every level of applied N, but both genotypes responded linearly to N with partial factor productivity for N (PFP N ) ranging from 14 to 19 for OPV versus 26–30 for hybrid, with improved N efficiencies obtained when P and K rates were significantly higher. Averaged across phosphorus (P) and potassium (K) levels, a $ 1 incremental investment in fertilizer increased the gross margin (GM) by $ 1.70 ha −1 in OPV and by $ 1.83 ha −1 in the hybrid. For the full response of N, requires higher rate of P 2 O 5 :K 2 O and vice-versa and full response to P 2 O 5 :K 2 O does not occur if N is absent. These results suggest that, i) degraded soils in the mid-hills of Nepal respond favorably to macronutrient fertilizers – even at high rates, ii) balanced fertilization is necessary to optimize returns on investments in N but must be weighed against additional costs, iii) OPVs benefit from investments in fertilizer, albeit at a PFP N that is 36–47% lower than for hybrids, and, consequently iv) hybrids are an effective mechanism for achieving a higher return on fertilizer investments, even when modest rates are applied. To extend these findings across years and sites in the mid-hills, crop growth simulations using the CERES-maize model (DSSAT) were conducted for 11 districts with historical weather and representative soils data. Average simulated (hybrid) maize yields with high fertilizer rate (180:60:60 kg N:P 2 O 5 :K 2 O ha −1 ) ranged from 3.9 t ha −1 to 7.5 t ha −1 across districts, indicating a high disparity in attainable yield potential. By using these values to estimate district-specific attainable yield targets, recommended N fertilizer rates vary between 65 and 208 kg N ha −1 , highlighting the importance of developing domain-specific recommendations. Simulations also suggest the potential utility of using weather forecasts in tandem with site and planting date information to adjust fertilizer recommendations on a seasonal basis. [ABSTRACT FROM AUTHOR]

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]

China is facing one of the largest challenges of this century to continue to increase annual cereal production to about 600 Mt by 2030 to ensure food security with shrinking cropland and limited resources, while maintaining or improving soil fertility, and protecting the environment. Rich experiences in integrated and efficient utilization of different strategies of crop rotation, intercropping, and all possible nutrient resources accumulated by Chinese farmers in traditional farming systems have been gradually abandoned and nutrient management shifted to over-reliance on synthetic fertilizers. China is now the world's largest producer, consumer and importer of chemical fertilizers. Overapplication of nitrogen (N) is common in intensive agricultural regions, and current N-uptake efficiency was reported to be only 28.3, 28.2 and 26.1% for rice, wheat and maize, respectively, and less than 20% in intensive agricultural regions and for fruit trees or vegetable crops. In addition to surface and groundwater pollution and greenhouse gas emissions, over-application of N fertilizers has caused significant soil acidification in major Chinese croplands, decreasing soil pH by 0.13 to 2.20. High yield as a top priority, small-scale farming, lack of temporal synchronization of nutrient supply and crop demand, lack of effective extension systems, and hand application of fertilizers by farmers are possible reasons leading to the over-application problems. There is little doubt that current nutrient management practices are not sustainable and more efficient management systems need to be developed. A review of long-term experiments conducted around the world indicated that chemical fertilizer alone is not enough to improve or maintain soil fertility at high levels and the soil acidification problem caused by overapplication of synthetic N fertilizers can be reduced if more fertilizer N is applied as NO relative to ammonium- or urea-based N fertilizers. Organic fertilizers can improve soil fertility and quality, but long-term application at high rates can also lead to more nitrate leaching, and accumulation of P, if not managed well. Well-managed combination of chemical and organic fertilizers can overcome the disadvantages of applying single source of fertilizers and sustainably achieve higher crop yields, improve soil fertility, alleviate soil acidification problems, and increase nutrient-use efficiency compared with only using chemical fertilizers. Crop yield can be increased through temporal diversity using crop rotation strategies compared with continuous cropping and legume-based cropping systems can reduce carbon and nitrogen losses. Crop yield responses to N fertilization can vary significantly from year to year due to variation in weather conditions and indigenous N supply, thus the commonly adopted prescriptive approach to N management needs to be replaced by a responsive in-season management approach based on diagnosis of crop growth, N status and demand. A crop sensor-based in-season site-specific N management strategy was able to increase Nuptake efficiency by 368% over farmers' practices in the North China Plain. Combination of these well-tested nutrient management principles and practices with modern crop management technologies is needed to develop sustainable nutrient management systems in China that can precisely match field-to-field and year-to-year variability in nutrient supply and crop demand for both single crops and crop rotations to not only improve nutrient-use efficiency but also increase crop yield and protect the environment. In addition, innovative and effective extension and service-providing systems to assist farmers in adopting and applying new management systems and technologies are also crucially important for China to meet the grand challenge of food security, nutrient-use efficiency and sustainable development. [ABSTRACT FROM AUTHOR]

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]