Site-specific Nitrogen Management Research Articles

Spatial-temporal variability in nitrogen use efficiency: Insights from a long-term experiment and crop simulation modeling to support site specific nitrogen management

Within-field soil heterogeneity can lead to large variation in nitrogen use efficiency (NUE). Crop simulation models provide a multi-faceted approach to management considering both soil and plant interactions. However, research using crop models for investigating within field variation in NUE is limited, in part because of challenges quantifying spatially variable soil model parameters. Here soil apparent electrical conductivity (ECa) and measured soil properties were used to map spatial variations in soil characteristics across a Long-Term Experiment in Norfolk, England. The relationship between plot ECa across the 3 ha experiment and agronomic data across three different nitrogen rates (0, 110, and 220 kg N ha-1) over five wheat years (2010–2020) was quantified. The Sirius crop model was parameterized for two soils representing the extremes of ECa. Sirius was validated using recorded plot data. Site-specific optimal nitrogen and associated leaching risks were simulated across 29 years of weather data. Variation in soil properties had significant impact on measured NUE. At 220 kg N ha-1 mean observed yields across 5 years ranged from 9.0 to 10.7 t ha-1 and grain protein from 11.6% to 11% on the low EC and high EC plots, respectively. On average fertiliser grain N recovery was 19.7 kg N ha-1 lower on the low ECa plots. Sirius simulated the variation in yield, grain protein and grain N recovery to a good level of accuracy with RRMSE of 19.5%, 15.4% and 19.5%, respectively. Simulated optimal nitrogen on the low EC soils was on average 12 kg N ha-1 lower, with >1 in 4 years with optimal nitrogen <200 kg N ha-1. Our work demonstrated that using a combination of proximal soil EC scans and targeted soil sampling we can optimize the data requirements for model parameterisation to support site-specific N management.

Growth, Yield and Quality of Rabi Groundnut (Arachis hypogaea L.) as Influenced by Sources of Calcium and Sulphur under Different Site - Specific Nitrogen Management in Rice-Groundnut Cropping System

The injudicious and imbalanced fertilizer use under the existing farming system necessitates the adoption of amelioration methods and balanced use of nutrients especially nitrogen (N). There is lack of information on effects of various sources of calcium and sulphur in groundnut along with balanced nitrogen management which needs to be addressed. A field experiment was carried out during 2020-21 and 2021-22 at Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India to study the growth, yield and quality parameters of groundnut as affected by site specific nitrogen management in rice and various sources of calcium and sulphur in groundnut. The experiment was laid out in split plot design with six main plot treatments i.e. Soil test based nitrogen (STBN) (100 kg N/ha), 75% N through STBN + 25% N through farmyard manure (FYM), 75% N through STBN + 25% N through vermicompost, N @ 20kg at basal and at leaf colour chart (LCC) &lt; 3, N @ 20kg at basal and at chlorophyll value determined by soil plant analysis development (SPAD) &lt; 35 and no nitrogen to rice during Kharif and three sub plot treatments i.e. lime @ 0.2 LR, gypsum @ 250 kg/ha and lime @ 0.2 LR + gypsum @ 250 kg/ha to groundnut during Rabi each in three replications. Application of 75% N through STBN + 25% N through vermicompost to preceding rice and lime @ 0.2 LR+ gypsum @ 250 kg/ha to groundnut crop resulted in highest plant height at harvest, number of nodules per plant and dry matter accumulation at harvest. Yield attributing characters like pods/plant, kernel/pod and 100 - pod weight of groundnut were highest due to application of 75% N through STBN + 25% N through vermicompost to preceding rice (17.86, 1.87 and 67.2 g, respectively) followed by 75% N through STBN + 25% N through FYM. Application of lime @ 0.2 LR + gypsum @ 250 kg/ha to groundnut recorded more pods/plant (16.86), kernels/pod (1.78) and 100 - pod weight (66.39 g). The treatment receiving 75% N through STBN + 25 % N through vermicompost in rice and application of lime @ 0.2 LR + gypsum @ 250 kg/ha to groundnut resulted significantly maximum pod yield (2,596 kg/ha and 2,291 kg/ha), haulm yield (4,554 kg/ha and 4,343 kg/ha) and harvest index (36.30% and 34.38%) in groundnut. Oil yield (682.2 kg/ha) in groundnut was recorded highest in the treatment lime @ 0.2 LR + gypsum @ 250 kg/ha followed by only gypsum @ 250 kg/ha application to groundnut. Similarly, application of lime @ 0.2 LR + gypsum @ 250 kg/ha to groundnut recorded highest protein yield (424.6 kg/ha).

Mapping Topsoil Total Nitrogen Using Random Forest and Modified Regression Kriging in Agricultural Areas of Central China

Accurate understanding of spatial distribution and variability of soil total nitrogen (TN) is critical for the site-specific nitrogen management. Based on 4337 newly obtained soil observations and 33 covariates, this study applied the random forest (RF) algorithm and modified regression kriging (RF combined with residual kriging: RFK, hereafter) model to spatially predict and map topsoil TN content in agricultural areas of Henan Province, central China. According to the RFK prediction, topsoil TN content ranged from 0.52 to 1.81 g kg-1, and the farmland with the topsoil TN contents of 1.00-1.23 g kg-1 and 0.80-1.23 g kg-1 accounted for 48.2% and 81.2% of the total farmland area, respectively. Spatially, the topsoil TN in the study area was generally higher in the west and lower in the east. By using the Boruta variable selection algorithm, soil organic matter (SOM) and available potassium contents in topsoil, nitrogen deposition, average annual precipitation, livestock discharges, and topsoil pH were identified as the main factors driving the spatial distribution and variation of soil TN in the study area. The RF and RFK models used showed the expected performance and achieved acceptable TN prediction accuracy. In comparison, RFK performed slightly better than the RF model. The R2 and RMSE achieved by the RFK model were improved by 4.5% and 4.5%, respectively, compared with that by the RF model. However, the results suggest that RFK was inferior to the RF model in quantifying prediction uncertainty and thus may have a slight disadvantage in model reliability.

Effect of Site Specific Nitrogen Management on Grain Quality and Productivity of kharif Rice

A field study was carried out during kharif at the Agronomy Main Research Farm, Department of Agronomy, College of Agriculture, OUAT, Bhubaneswar with the crop rice. The experiment was laid out in randomized complete block design with twelve site specific nitrogen management practices i.e. T1-RDF (80:40:40 kg N:P2O5:K2O ha-1), T2 - 20 kg N ha-1 at LCC score &lt;4 with no basal N, T3 - 20 kg N ha-1 at LCC score &lt;4 with 20 kg N ha-1 at basal, T4 - 20 g N ha-1 at SPAD value &lt;35 with no basal N, T5- 20 kg N ha-1 at SPAD value &lt;35 with 20 kg N ha-1 at basal, T6- 20 kg N ha-1 at Green seeker (GS) &gt;1.25 RI with no basal N, T7- 20 kg N ha-1 at Green seeker (GS) &gt;1.25 RI with 20 kg N ha-1 at basal, T8- 150 kg N ha-1 (N rich strip plot), T9- N omission plot, T10- STBN, T11- INM (20 kg N ha-1 as FYM at basal + 40 kg N ha-1 at tillering + 20 kg N ha-1 at PI stage), T12- Organic nutrient (FYM 10t ha-1 at basal + 2t ha-1 VC top dressing). The result of the experiment revealed that protein and carbohydrate yield were higher with application of 20 kg N ha-1 at Green seeker (GS) &gt;1.25 RI with 20 kg N ha-1 at basal (410.4 and 4135.1 kg ha-1, respectively) followed by the treatment receiving 20 Kg N ha-1 at SPAD value &lt;35 with 20 Kg N ha-1 at basal (395.5 and 3977.2 kg ha-1, respectively). Application of 20 kg N ha-1 at Green seeker (GS) &gt;1.25 RI with 20 kg N ha-1 at basal recorded significantly higher grain yield (5.68 t ha-1), straw yield (6.57 t ha-1) and harvest index (47 %) followed by 20 kg N ha-1 at SPAD value &lt;35 with 20 kg N ha-1 at basal.

Impact of Site Specific Nitrogen Management on Growth Parameters, Productivity and Profitability of Kharif rice

A field experiment was carried out during kharif 2021 (June-November) and 2022 (June-November) at the Agronomy Main Research Farm, Department of Agronomy, College of Agriculture, OUAT, Bhubaneswar. The experiment was laid out in randomized complete block design with twelve treatments i.e. T1-RDF (80:40:40 kg N:P2O5:K2O ha-1), T2 - 20 kg N ha-1 at LCC score &lt;4 with no basal N, T3 - 20 kg N ha-1 at LCC score &lt;4 with 20 kg N ha-1 at basal, T4 - 20 g N ha-1 at SPAD value &lt;35 with no basal N, T5- 20 kg N ha-1 at SPAD value &lt;35 with 20 kg N ha-1 at basal, T6- 20 kg N ha-1 at Green seeker (GS) &gt;1.25 RI with no basal N, T7- 20 kg N ha-1 at Green seeker (GS) &gt;1.25 RI with 20 kg N ha-1 at basal, T8- 150 kg N ha-1 (N rich strip plot), T9- N omission plot, T10- STBN, T11- INM (20 kg N ha-1 as FYM at basal + 40 kg N ha-1 at tillering + 20 kg N ha-1 at PI stage), T12- Organic nutrient (FYM 10t ha-1 at basal + 2t ha-1 VC top dressing). Pooled analysis revealed that application of 20 kg N ha-1 at Green seeker (GS) &gt;1.25 RI with 20 kg N ha-1 at basal recorded maximum dry matter accumulation (1194.1 g m-2), larger leaf area index (3.30), highest number of tillers (381 m-2) at harvest and maximum CGR of 18.73 g m-2 day-1 during 60 to 80 DAT followed by the treatment receiving 20 Kg N ha-1 at SPAD value &lt;35 with 20 Kg N ha-1 at basal. Combined analysis of both years showed that application of 20 kg N ha-1 at Green seeker (GS) &gt;1.25 RI with 20 kg N ha-1 at basal recorded significantly higher grain yield (5.68 t ha-1), straw yield (6.57 t ha-1) and harvest index (47 %) with maximum net return 55982 Rs ha-1 and B:C ratio 1.82.

Mid-season proximal sensing for site-specific need-based fertilizer nitrogen management in spring maize

Canopy reflectance measurements using active optical sensors have emerged as potential solution to draw in-season nitrogen (N) topdressings in cereals. Site-specific need-based N management strategy using a GreenSeeker optical sensor was developed and evaluated in spring maize. Four field experiments were conducted at two locations during 2017 and 2018 to develop an algorithm, and two experiments during 2019 to validate the sensor-based application for improving N use efficiency (NUE) and grain yield. The sensor measurements made at V9 growth stage with R2 value of 0.61 obtained from the relation between in-season estimate of yield (INSEY) and grain yield rendered better corrective N rate for improving NUE and getting optimum grain yield, whereas sensor measurements at V6, V12 and VT growth stages were not reliable for the purpose. Applying N rate in two splits of 30 kg N ha−1 at planting and 45 kg N ha−1 at V6 growth stage was recommended before applying sensor based corrective N rate at V9 stage. Sensor guided N topdressing produced yields similar to those obtained by following the soil test-based N recommendations but with lower N rates. These results were also reflected in considerable increase in N recovery (8 _18%) and agronomic (4 _13.5 kg grain kg−1 N) efficiencies in comparison with soil test-based N recommendations, thereby signifying the usefulness of the sensor-based algorithm in optimizing need-based fertilizer N management in spring maize.

Broadacre Mapping of Wheat Biomass Using Ground-Based LiDAR Technology

Crop biomass is an important attribute to consider in relation to site-specific nitrogen (N) management as critical N levels in plants vary depending on crop biomass. Whilst LiDAR technology has been used extensively in small plot-based phenomics studies, large-scale crop scanning has not yet been reported for cereal crops. A LiDAR sensing system was implemented to map a commercial 64-ha wheat paddock to assess the spatial variability of crop biomass. A proximal active reflectance sensor providing spectral indices and estimates of crop height was used as a comparison for the LiDAR system. Plant samples were collected at targeted locations across the field for the assessment of relationships between sensed and measured crop parameters. The correlation between crop biomass and LiDAR-derived crop height was 0.79, which is similar to results reported for plot scanning studies and greatly superior to results obtained for the spectral sensor tested. The LiDAR mapping showed significant crop biomass variability across the field, with estimated values ranging between 460 and 1900 kg ha−1. The results are encouraging for the use of LiDAR technology for large-scale operations to support site-specific management. To promote such an approach, we encourage the development of an automated, on-the-go data processing capability and dedicated commercial LiDAR systems for field operation.

Economic evaluation of variable rate nitrogen management of canola for zones based on historical yield maps and soil test recommendations

Canola (Brassica napus L.) is a highly valuable crop for Canada’s economy, making the efficient management of canola a priority. A field-scale study was conducted at ten sites between 2014 and 2016 to evaluate the viability of site specific nitrogen (N) management zones (MZ) based on analysis of historical yield maps and soil test recommendations to improve canola productivity and profitability in western Canada. Treatments included factorial combinations of three canola yield zones (low, average, high) by four N rates, replicated four times at each site. The canola yield function had a quadratic form in each field but the effects of MZ varied between fields with positive effects in only a few fields. When ten site-years data were combined, MZ had positive effects on canola performance. On average, MZ of N fertilizer over ten fields generated between $28 to $65 ha−1 more net revenue (NR) relative to average yield management. Site-years, which reflect farm management and other farm characteristics had significant effects on yield and NR ranging from − $91 to $352 ha−1 compared to a baseline. Nitrogen application under MZs was only reduced by 8% compared to uniform rates. The potential for MZ does exist; however, its effectiveness is highly variable.

Effect of Site Specific Nitrogen Management on Seed Nitrogen—A Driving Factor of Winter Oilseed Rape (Brassica napus L.) Yield

It has been assumed that the management of both soil and fertilizer N in winter oilseed rape (WOSR) is crucial for N accumulation in seeds (Nse) and yield. This hypothesis was evaluated based on field experiments conducted in 2008/09, 2009/10, 2010/11 seasons, each year at two sites, differing in soil fertility, including indigenous N (Ni) supply. The experimental factors consisted of two N fertilizers: N and NS, and four Nf rates: 0, 80, 120, 160 kg ha−1. Yield, as governed by site × Nf rate interaction, responded linearly to Nse at harvest. The maximum Nse (Nsemax), as evaluated by N input (Nin = Ni + Nf) to WOSR at spring regrowth, varied from 95 to 153 kg ha−1, and determined 80% of yield variability. The basic reason of site diversity in Nsemax was Ni efficiency, ranging from 46% to 70%, respectively. The second cause of Nse variability was a shortage of N supply from + 9.5 soil to −8.8 kg ha−1 to the growing seeds during the seed filling period (SFP). This N pool supports the N concentration in seeds, resulting in both seed density and a seed weight increase, finally leading to a yield increase.

Economic Optimal Nitrogen Rate Variability of Maize in Response to Soil and Weather Conditions: Implications for Site-Specific Nitrogen Management

The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha−1) with three planting densities (55,000, 70,000, and 85,000 plant ha−1) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha−1) than in aeolian sandy soil (186 kg ha−1). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha−1, 176 $ ha−1, and 163 $ ha−1, and improve N use efficiency (NUE) by 37–42%, 52%, and 67–71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7–8%, 16–19% and 28–34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year’s weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions.

Site-specific nitrogen management in winter wheat supported by low-altitude remote sensing and soil data

Site-specific nitrogen (N) management in precision agriculture is used to improve nitrogen use efficiency (NUE) at the field scale. The objective of this study has been (i) to better understand the relationship between data derived from an unmanned aerial vehicle (UAV) platform and the crop temporal and spatial variability in small fields of about 2 ha, and (ii) to increase knowledge on how such data can support variable application of N fertilizer in winter wheat (Triticum aestivum). Multi-spectral images acquired with a commercially available UAV platform and soil available mineral N content (Nmin) sampled in the field were used to evaluate the in-field variability of the N-status of the crop. A plot-based field experiment was designed to compare uniform standard rate (ST) to variable rate (VR) N application. Non-fertilized (NF) and N-rich (NR) plots were placed as positive and negative N-status references and were used to calculate various indicators related to NUE. The crop was monitored throughout the season to support three split fertilizations. The data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits. Grain yield was mostly in the expected range and inconsistently higher in VR compared to ST. In contrast, N fertilizer application was reduced in the VR treatments between 5 and 40% depending on the field heterogeneity. The study showed that the methods used provided a good base to implement variable rate fertilizer application in small to medium scale agricultural systems. In the majority of the case studies, NUE was improved around 10% by redistributing and reducing the amount of N fertilizer applied. However, the prediction of the N-mineralisation in the soil and related N-uptake by the plants remains to be better understood to further optimize in-season N-fertilization.

Spatial correlation between the chlorophyll index and foliar npk levels in coffee crop

Detection of spatial variability of data that can improve crop management is a key factor for precision agriculture. In agriculture, there is a need for tools to assist farmers in decision-making about proper nutrient management, aiming to achieve their full productive potential. Based on that, this study aimed to (1) determine the spatial correlations between the chlorophyll index (CI) and the foliar levels of nitrogen, phosphorus and potassium (NPK) in the coffee crop using geostatistical tools; and (2) to evaluate the potential use of this index as a tool for site-specific nutrient management in an irrigated coffee field. For that, a study was carried out in a 2.1 ha area under arabica coffee cultivation in Paula Cândido, Minas Gerais State, Brazil. Samplings of the CI were performed in 1141 plants using a portable chlorophyll meter (SPAD-502). Regarding the NPK analysis, leaf samples from one of each 10 plants used to measure the CI were taken for chemical analysis (114 plants). Then, the data were submitted to descriptive and geostatistical analysis. For the spatial correlation analysis, the Moran Bivariate Global (I) and the Local index (Ixy) were used. The results showed a moderate correlation between the CI and N (0.500), showing the potential of the chlorophyll meter as a tool for site-specific nitrogen management in the coffee crop. Differently, the CI is not recommended for P and K management since they were not well correlated. Lastly, as a tool that performs indirect measurements, the results from the chlorophyll meter should be validated by field measurements to local calibrations.

Profitability and downside risk implications of site-specific nitrogen management with respect to wheat grain quality

Profitability analyses of site-specific nitrogen (N) management strategies have often failed to provide satisfying reasons for adoption of precision farming technologies. However, effects of precision farming on product quality and price premiums, as well as on downside risk mitigation, are generally not taken into account. This study aimed to evaluate the comparative advantages of site-specific N management over uniform N management considering N supply on grain quality, and accordingly price premiums for wheat from a downside risk point of view. A virtual field was modelled with two subfields representing two distinctive yield zones to investigate how consideration of grain quality affects the economic potential of site-specific N management under temporally varying N mineralization and changing price patterns of wheat. Moreover, the extent was investigated to which site-specific N management can have a risk-reducing effect on economic shortfalls compared to uniform N management. Two site-specific N management options were assessed: variable N rate application using yield mapping and N sensor for real-time proximal sensing. Results indicated that even though crop yields were only slightly higher, higher expected protein contents of grains could be achieved with site-specific N management options compared to uniform N management. Baking wheat quality was secured to a greater extent with site-specific N management options. Higher average grain quality improved the economic benefits due to price premiums. A risk-reducing effect was observed with the site-specific N management by maintaining the baking wheat quality with a higher probability. Higher economic returns mostly compensated the additional costs for the precision farming technologies in the lower tail of the probability distribution and, thus, site-specific N management did not show any substantial disadvantage on downside risk as compared to uniform N management.

Water effects on optical canopy sensing for late-season site-specific nitrogen management of maize

The interpretation of optical canopy sensor readings for determining optimal rates of late-season site-specific nitrogen application to corn (Zea mays L.) can be complicated by spatially variable water sufficiency, which can also affect canopy size and/or pigmentation. In 2017 and 2018, corn following corn and corn following soybeans were subjected to irrigation × nitrogen fertilizer treatments in west central Nebraska, USA, to induce variable water sufficiency and variable nitrogen sufficiency. The vegetation index-sensor combinations investigated were the normalized difference vegetation index (NDVI), the normalized difference red edge index (NDRE), and the reflectance ratio of near infrared minus red edge over near infrared minus red (DATT) using ACS-430 active optical sensors; NDVI using SRS-NDVI passive optical sensors; and red brightness and a proprietary index using commercial aerial visible imagery. Among these combinations, NDRE and DATT were found to be the most suitable for assessing nitrogen sufficiency within irrigation levels. While DATT was the least sensitive to variable water sufficiency, DATT still tended to decrease with decreasing water sufficiency in high nitrogen treatments, whereas the effect of water sufficiency on DATT was inconsistent in low nitrogen treatments. A new method of quantifying nitrogen sufficiency while accounting for water sufficiency was proposed and generally provided more consistent improvement over the mere averaging of water effects as compared with the canopy chlorophyll content index method. Further elucidation and better handling of water-nitrogen interactions and confounding are expected to become increasingly important as the complexity, automation, and adoption of sensor-based irrigation and nitrogen management increase.

Monitoring Wheat (Triticum aestivum L.) Leaf Nitrogen using Diagnostic Tools for Fertilizer Nitrogen Management

A field experiment was conducted to evaluate the leaf colour chart (LCC) and chlorophyll meter (SPAD and fieldscout chlorophyll meter (FSCM)) for developing site specific nitrogen (N) management strategy in wheat (Triticum aestivum L.). Four levels of fertilizer N (0, 70, 140 and 210 kg ha−1) were used to determine the growth stage specific optimum LCC, SPAD and FSCM chlorophyll meter values for achieving economic optimum grain yield. A positive and significant (R2 = 0.93–0.97, p<0.01) quadratic response between N application levels and wheat grain yield was observed. Economic optimum grain yield of 4.68 t ha−1 (first year) and 4.57 t ha−1 (second year) corresponding to economic optimum dose of 141 and 144 kg N ha−1, respectively were observed using quadratic response function. Nitrogen concentration in fully expanded youngest leaf correlated significantly (p<0.01) and positively with LCC score (r = 0.89), SPAD value (r = 0.94), FSCM value (r = 0.68) and total chlorophyll concentration (r = 0.91). Two chlorophyll meters used in the study were calibrated with a significant (p<0.01) linear response as ‘FSCM value = 11.3×SPAD value-221.5’ (R2 = 0.60). The LCC score, SPAD and FSCM values observed at maximum tillering, booting and flowering stages were highly and significantly correlated with wheat grain yield. The optimum LCC score, SPAD values, FSCM values, leaf chlorophyll and N concentration at maximum tillering (most important crop growth stage for N management) to fetch economic optimum grain yield were found to be 4.5, 45, 295, 2.47 mg g−1 and 2.74%, respectively. Growth stage specific optimum and critical values of the above mentioned leaf N indicators for achieving economic optimum and critical grain yield of wheat crop were developed in the present investigation.

English

English

Using site-specific nitrogen management in rainfed corn to reduce the risk of nitrate leaching

Managing nitrogen (N) to achieve yield potential and limit losses to the environment is challenging due to the temporal and spatial variability in crop N uptake which affects the distribution of soil-N. Nitrogen fertilization using site-specific management (SSM) is one of a number of strategies that can improve the efficiency of N use and reduce the losses of N to the environment from cropping systems. The aim was to assess: (i) corn (Zea mays L.) grain yield and N uptake; and (ii) soil residual- and potentially leachable-N, and its relationship with N and water use efficiency using SSM vs. uniform management (UM) strategies in high-(HP) and low-(LP) productivity zones on soils of the Inland Pampas of Argentina. Differences in soil residual- and potentially leachable-N, corn grain yield, N uptake, water and N use efficiency were compared between treatments. In HP-zones, corn grain yield and total biomass were 2.7 and 4.2 Mg ha−1 higher with SSM than UM, and corn grain N uptake and total N uptake increased by 21% and 18% with SSM when compared to UM. Soil residual-N at field-scale was reduced by 18% with SSM. Marginal differences in potentially leachable-N among treatments were observed throughout the soil profile; the highest nitrate concentration was 6.6 mg kg−1 in LP-zones with UM within the 210–240 cm soil layer. Overall corn water use efficiency in total biomass was 16% higher with SSM than with UM in both LP- and HP-zones. Using SSM in the LP-zones increased corn N use efficiency in grain and total biomass by 50% and 43% respectively. In this context, SSM can be considered as a conservation practice that optimizes N and water use efficiency by corn under dry conditions.

English

English

Site-Specific Nitrogen Management in Rice Using Remote Sensing and Geostatistics

ABSTRACTProper doses of nitrogenous fertilizer are most important for rice production system because a large part of the nitrogen may be lost if it is not applied judiciously. A study was conducted covering five blocks of Balasore and two blocks of Bhadrak districts. Soil samples were collected randomly, and field visit was conducted during peak vegetative stage of rice. Two approaches have been used in this study for estimating the site-specific nitrogen (N) requirement in the study area. In one approach, geostatisical analysis and kriging was used to develop the soil test–based N recommendation map by which a minimum of 72 kg N ha−1 and maximum of 94 kg N ha−1 were recommended. In a second approach, remote sensing was used and N recommendation map was developed using the moderate-resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and normalized difference vegetation index (NDVI) satellite data, and a minimum requirement of 60 kg N ha−1 and maximum of 120 kg N ha−1 was estimated through this approach.

Potential of RapidEye and WorldView-2 Satellite Data for Improving Rice Nitrogen Status Monitoring at Different Growth Stages

For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large areas within a short time frame. In recent studies, the FORMOSAT-2 satellite images with traditional blue (B), green (G), red (R), and near-infrared (NIR) wavebands have been used to estimate rice N status due to its high spatial resolution, daily revisit capability, and relatively lower cost. This study aimed to evaluate the potential improvements of RapidEye and WorldView-2 data over FORMOSAT-2 for rice N status monitoring, as the former two sensors provide additional wavelengths besides the traditional four wavebands. Ten site-year N rate experiments were conducted in Jiansanjiang, Heilongjiang Province of Northeast China from 2008 to 2011. Plant samples and field hyperspectral data were collected at three growth stages: panicle initiation (PI), stem elongation (SE), and heading (HE). The canopy-scale hyperspectral data were upscaled to simulate the satellite bands. Vegetation index (VI) analysis, stepwise multiple linear regression (SMLR), and partial least squares regression (PLSR) were performed to derive plant N status indicators. The results indicated that the best-performed VIs calculated from the simulated RapidEye and WorldView-2 bands, especially those based on the red edge (RE) bands, explained significantly more variability for above ground biomass (AGB), plant N uptake (PNU), and nitrogen nutrition index (NNI) estimations than their FORMOSAT-2-based counterparts did, especially at the PI and SE stages. The SMLR and PLSR models based on the WorldView-2 bands generally had the best performance, followed by the ones based on the RapidEye bands. The SMLR results revealed that both the NIR and RE bands were important for N status estimation. In particular, the NIR1 band (760–900 nm from RapidEye or 770–895 nm from WorldView-2) was most important for estimating all the N status indicators. The RE band (690–730 nm or 705–745 nm) improved AGB, PNU, and NNI estimations at all three stages, especially at the PI and SE stages. AGB and PNU were best estimated using data across the stages while plant N concentration (PNC) and NNI were best estimated at the HE stage. The PLSR analysis confirmed the significance of the NIR1 band for AGB, PNU, and NNI estimations at all stages except for the HE stage. It also showed the importance of including extra bands (coastal, yellow, and NIR2) from the WorldView-2 sensor for N status estimation. Overall, both the RapidEye and WorldView-2 data with RE bands improved the results relative to FORMOSAT-2 data. However, the WorldView-2 data with three extra bands in the visible and NIR regions showed the highest potential in estimating rice N status.