Site-specific Agricultural Management Research Articles

A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine

High-resolution crop type mapping is of importance for site-specific agricultural management and food security in smallholder farming regions, but is challenging due to limited data availability and the need for image-based algorithms. In this paper, we developed an efficient object- and pixel-based mapping algorithm to generate a 10 m resolution crop type map over large spatial domains by integrating time series optical images (Sentinel-2) and synthetic aperture radar (SAR) images (Sentinel-1) using the Google Earth Engine (GEE) platform. The results showed that the proposed method was reliable for crop type mapping in the study area with an overall accuracy (OA) of 93.22% and a kappa coefficient (KC) of 0.89. Through experiments, we also found that the monthly median values of the vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH) bands were insensitive to crop type mapping itself, but adding this information to supplement the optical images improved the classification accuracy, with an OA increase of 0.09–2.98%. Adding the slope of vegetation index change (VIslope) at the critical period to crop type classification was obviously better than that of relative change ratio of vegetation index (VIratio), both of which could make an OA improvement of 2.58%. These findings not only highlighted the potential of the VIslope and VIratio indices during the critical period for crop type mapping in small plots, but suggested that SAR images could be included to supplement optical images for crop type classification.

Within-Field Variability in Nutrients for Site-Specific Agricultural Management in Irrigated Cornfield

HighlightsPhosphorus and nitrogen in soil and plants varied during the growing season across the cornfield.Spatial variability in nutrients caused spatial variability in plant growth and crop yield across the cornfield.Hydrological characteristics and soil hydraulic properties were responsible for spatial variability in nutrients.Need to consider spatial variability in nutrients in delineating management zones before adopting management practices.Abstract. The adoption of precision agricultural practices in Alabama has been increasing in recent years. Although challenging, understanding nutrient variability in agricultural fields is important for site-specific management. This study investigated phosphorus (P) and nitrogen (N) variability in an irrigated cornfield located in the Town Creek Watershed of Tennessee Valley Region of Alabama, USA during the 2019 growing season. Three different irrigation management zones (high yield-HY, moderate yield-MY, and low yield-LY) were delineated based on ten years of historical records of crop yield, soil texture, and measured topography using the Management Zone Analyst (MZA) software. The soil samples at 0 to 15 cm, 15 to 30 cm, and 30 to 60 cm depths were collected five different times during the 2019 growing season. Both manure and inorganic fertilizer were uniformly applied during the growing season. At all soil depths, HY and MY zones had higher soil nutrient concentrations than the field average nutrient concentration. However, the LY zone had below field average soil nutrient concentration in the entire soil profile. The plants (above-ground) in HY and MY zones had higher than average nutrient concentrations and the plants in the LY zone had lower than average nutrient concentrations throughout the cornfield. This site-specific nutrient variation in these zones was due to within-field terrain attributes, differences in soil properties, and surface runoff losses. A significant difference was recorded in the corn yield in the HY and LY zones of the field. Two extreme groups of nutrients were found in the cornfield similar to irrigation management zones (HY and LY). A zone with a higher yield also had higher soil and plant nutrients in the zone (HY), and a zone with a lower yield also had lower soil and plant nutrients in the zone (LY). Incorporating nutrient variability for site-specific management in management zone delineation can help with reducing nutrient application, nutrient loss, and improving yield. This study supports the adopting of precision agricultural management practices based on in-field nutrient variability. Keywords: Corn, Crop yield variability, Irrigation, Management zones, Nitrogen, Nutrient variability, Phosphorus, Site-specific management, Topographical wetness index (TWI).

Soil Fertility Changes With Climate and Island Age in Galápagos: New Baseline Data for Sustainable Agricultural Management

While the extended absence of human influence has led to matchless natural conditions on the Galápagos archipelago, agricultural activities on the inhabited islands are increasingly affecting soil health and functioning. However, a systematic assessment of the agricultural soils on the Galápagos Islands is still absent. Plate tectonics and hotspot volcanism cause an eastward drift of the archipelago and result in a west-to-east soil age gradient from approx. 1 to 1,000 ka. In addition, precipitation regimes change from arid to humid with elevation on the higher-standing islands. The objective of this study was to investigate differences in soil fertility parameters and Mehlich (III)-extractable nutrient levels along these gradients in order to provide baseline information for sustainable agricultural management. Topsoil samples (0–20 cm) from 125 farms of the islands Isabela, Santa Cruz and San Cristóbal were analyzed. Gravel and sand content, pH, electrical conductivity, cation exchange capacity, base saturation, soil organic C and total N content tended to decrease with increasing island age, while clay content, soil macroaggregate stability, plant-available water and bulk density increased. Mehlich (III)-extractable base cations Ca, K, Mg and Na as well as P, Fe and Zn showed a decreasing tendency from the youngest to the oldest island, while Mn increased with island age. Mehlich (III)-extractable Cu and Na reached their highest levels on the most intensively farmed, intermediate-aged island Santa Cruz, likely related to anthropogenic inputs and irrigation with brackish water, respectively. Changes along the altitudinal climate gradient within the studied islands were most significant for soil pH, base saturation, and Mehlich (III)-extractable Ca and Mn. Our results highlight the importance of site-specific agricultural management to account for the strong heterogeneity in soil parameters among and within the Galápagos archipelago. The data provided herein shall serve as a baseline for targeted future management strategies to avoid soil degradation, restore and maintain soil functioning and, hence, sustain the soils’ provision of ecosystem services in this unique archipelago.

Spatial variability of soil properties in red soil and its implications for site-specific fertilizer management

Assessing spatial variability and mapping of soil properties constitute important prerequisites for soil and crop management in agricultural areas. To explore the relationship between soil spatial variability and land management, 256 samples were randomly collected at two depths (surface layer 0–20 cm and subsurface layer 20–40 cm) under different land use types and soil parent materials in Yujiang County, Jiangxi Province, a red soil region of China. The pH, soil organic matter (SOM), total nitrogen (TN), cation exchange capacity (CEC), and base saturation (BS) of the soil samples were examined and mapped. The results indicated that soils in Yujiang were acidified, with an average pH of 4.87 (4.03–6.46) in the surface layer and 4.99 (4.03–6.24) in the subsurface layer. SOM and TN were significantly higher in the surface layer (27.6 and 1.50 g kg−1, respectively) than in the subsurface layer (12.1 and 0.70 g kg−1, respectively), while both CEC and BS were low (9.0 and 8.0 cmol kg−1, 29 and 38% for surface and subsurface layers, respectively). Paddy soil had higher pH (mean 4.99) than upland and forest soils, while soil derived from river alluvial deposits (RAD) had higher pH (mean 5.05) than the other three parent materials in both layers. Geostatistical analysis revealed that the best fit models were exponential for pH and TN, and spherical for BS in both layers, while spherical and Gaussian were the best fitted for SOM and CEC in the surface and subsurface layers. Spatial dependency varied from weak to strong for the different soil properties in both soil layers. The maps produced by selecting the best predictive variables showed that SOM, TN, and CEC had moderate levels in most parts of the study area. This study highlights the importance of site-specific agricultural management and suggests guidelines for appropriate land management decisions.

A comparison between spatial clustering models for determining N-fertilization management zones in orchards

Site-specific agricultural management (SSM) relies on identifying within-field spatial variability and is used for variable rate input of resources. Precision agricultural management commonly attempts to integrate multiple datasets to determine management zones (MZs), homogenous units within the field, based on spatial characteristics of environmental and crop properties (i.e., terrain, soil, vegetation conditions). This study compared several multivariate spatial clustering methods to determine MZs for precision nitrogen fertilization in a citrus orchard. Six variables, namely normalized difference vegetation index, crop water stress index, digital surface model, slope, elevation and aspect, were used to characterize spatial variability within four plots. Six clustering model composites were compared, each including some or all of the following components: (1) spatial representation of the data (e.g., Getis Ord Gi*); (2) variable weights based on their relative contribution; and (3) clustering methods, including different extensions of K-means and hierarchical clustering algorithms. The fuzzy K-means algorithm applied to the weighted spatial representation was found to generate MZs with similar numbers of trees, while the K-means algorithm applied over the spatial representation generated MZs that were more continuous over space, with minimum fragmentation. Spatial variability was not constant across the orchard and among the different variables. Management of the sub-units, or plots, using spatial representation rather than the measured values, is proposed as a more suitable platform for agricultural practices. SSM is dependent upon available variable rate application technologies. Future development of fertilizer application for individual trees will require adjusting the statistical approach to support tree-specific management. The suggested model composite is flexible and may be composed of different models for delineating plot-specific MZs.

FastMapping: Software to create field maps and identify management zones in precision agriculture

Diverse on-farm agronomic data, gathered via precision agriculture (PA), require depuration and joint analysis of multiple georeferenced field characteristics such as crop yields, and soil and topographic aspects. Multivariate analysis of spatial variability has been recommended to understand variation of data within fields and classify sites into zones of broad similarity to support crop management. However, most of the statistical techniques available for spatial data require advanced skills. Cleaning and statistical analysis of big spatial data can only be effectively implemented if there are easy-to-use computer programs integrating the analytical steps. This paper explains the development and implementation of FastMapping, an interactive web application to automatically clean PA raw data, generate spatial variability field maps, and delineate multivariate management zones. The application uses an interface developed in R language that automatically depurates datafiles; in addition, through automatic spatial interpolation of each variable, it allows us to merge data layers on the same spatial grid in such a way that each site within the agricultural field has values of all the measured variables. On this grid, FastMapping identifies homogeneous zones, in a multivariate and spatial way, and provides data reports including validation of the delineated zones. FastMapping outputs for zone delineation were compared with results from Management Zone Analyst (MZA) software in several fields. The new software yielded a similar zoning to that of MZA and added graphs and statistical results that are lacking in most PA software tools. The flexibility of FastMapping to import, clean and visualize PA data in a single computer environment is highly encouraging. FastMapping can be used by the PA community to support site-specific agricultural management.

Soil Physical Properties Spatial Variability under Long-Term No-Tillage Corn

Spatial variability of soil physical and hydrological properties within or among agricultural fields could be intrinsically induced due to geologic and pedologic soil forming factors, but some of the variability may be induced by anthropogenic activities such as tillage practices. No-tillage has been gaining ground as a successful conservation practice, and quantifying spatial variability of soil physical properties induced by no-tillage practices is a prerequisite for making appropriate site-specific agricultural management decisions and/or reformulating some management practices. In particular, there remains very limited information on the spatial variability of soil physical properties under long-term no-tillage corn and tropical soil conditions. Therefore, the main objective of this study was to quantify the spatial variability of some selected soil physical properties (soil surface temperature (ST), volumetric water content (θv), soil resistance (TIP), total porosity (θt), bulk density (ρb), organic carbon, and saturated hydraulic conductivity (Ksat)) using classical and geostatistical methods. The study site was a 2 ha field cropped no-tillage sweet corn for nearly 10 years on Oahu, Hawaii. The field was divided into 10 × 10 and 20 × 20 m grids. Soil samples were collected at each grid for measuring ρb, θt, and soil organic carbon (SOC) in the laboratory following standard methods. Saturated hydraulic conductivity, TIP at 10 and 20 cm depths, soil surface temperature, and θv were also measured. Porosity and ρb have low and low to moderate variability, respectively based on the relative ranking of the magnitude of variability drawn from the coefficient of variation. Variability of the SOC, TIP, and Ksat ranges from moderate to high. Based on the best-fitted semivariogram model for finer grid data, 9.8 m and 142.2 m are the cut off beyond which the measured parameter does not show any spatial correlation for SOC, and TIP at 10 cm depth, respectively. Bulk density shows the highest spatial dependence (range = 226.8 m) among all measured properties. Spatial distribution of the soil properties based on kriging shows a high level of variability even though the sampled field is relatively small.

Spatial variability of soil properties in the floodplain of a river oasis in the Mongolian Altay Mountains

In Mongolia, recent national policy pushed for the expansion and intensification of land use for crop and forage production, which may adversely affect soil quality of river oases. A better understanding of the small-scale variation of soil properties and the driving factors of soil quality are important for developing and refining site-specific agricultural management practices and for maintaining soil fertility. The current study hypothesized that spatial variability structure of ancient sedimentary processes override the actual variation of soil properties caused by land-use and environmental effects. The objectives were (i) to determine the small-scale variability of major topsoil properties within an exemplary river oasis in Western Mongolia, (ii) to assess the spatial structure of this variability (heterogeneity), and (iii) to identify factors driving the heterogeneity of soil biological properties. Within a four ha plot, 130 topsoil samples were collected in a constant 20 m grid and analyzed for major physico-chemical and microbial properties. The soil variability was assessed by descriptive statistics, the heterogeneity of properties by semivariogram model parameters, and heterogeneity driving factors by multiple linear regression analysis. Soil parameters showed a relatively high variability, particularly of CaCO3-C and EC (86% and 163%, respectively). In contrast to that, both parameters showed a relatively low heterogeneity (nugget to sill ratio of ≥0.67) whereas most soil parameter were characterized by a moderate or strong spatial dependency (nugget to sill ratio down to 0.2 as for microbial biomass C) and by relatively low range values (down to 37 m as for ergosterol and microbial biomass C), respectively. This small-scale heterogeneity with relatively many small patches reflected inherent soil factors of the floodplain, which had a strong influence on soil properties as the extensive land use in the river oasis Bulgan Sum Center did not lead to a homogenization of the fluvial depositions. Besides the minor salinization, which had a positive effect on ergosterol and basal respiration, SOC determined biological soil properties. This underlines the significance of SOC to preserve the scarce and susceptible agro-ecological resources of the river oasis in Western Mongolia and likely many similar ones across Central Asia.

Soil polygon disaggregation through similarity-based prediction with legacy pedons

Conventional soil maps generally contain one or more soil types within a single soil polygon. But their geographic locations within the polygon are not specified. This restricts current applications of the maps in site-specific agricultural management and environmental modelling. We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under- or over-sampled legacy pedon data for the disaggregation. The method consisted of three steps. First, environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors. Second, according to soil types of the pedon sites, the similarities were aggregated to derive similarity distribution for each soil type. Third, a hardening process was performed on the maps to allocate candidate soil types within the polygons. The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba, Canada. Based on 186 independent pedon sites, the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62. The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map, which was commonly used in practice. Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy, indicating that new environmental covariates need to be developed. We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.

A modeling study on mitigation of N2O emissions and NO3 leaching at different agricultural sites across Europe using LandscapeDNDC

The identification of site-specific agricultural management practices in order to maximize yield while minimizing environmental nitrogen losses remains in the center of environmental pollution research. Here, we used the biogeochemical model LandscapeDNDC to explore different agricultural practices with regard to their potential to reduce soil N2O emissions and NO3 leaching while maintaining yields. In a first step, the model was tested against observations of N2O emissions, NO3 leaching, soil micrometeorology as well as crop growth for eight European cropland and grassland sites. Across sites, LandscapeDNDC predicts very well mean N2O emissions (r2=0.99) and simulates the magnitude and general temporal dynamics of soil inorganic nitrogen pools. For the assessment of site-specific mitigation potentials of environmental nitrogen losses a Monte Carlo optimization technique considering different agricultural management options (i.e., timing of planting, harvest and fertilization, amount of applied fertilizer as well as residue management) was used. The identified optimized field management practices reduce N2O emissions and NO3 leaching from croplands on average by 21% and 31%, respectively. Likewise, average reductions of 55% for N2O emissions and 16% for NO3 leaching are estimated for grasslands. For mitigating environmental loss - while maintaining yield levels - it was most important to reduce fertilizer application rates by in average 10%. Our analyses indicate that yield scaled N2O emissions and NO3 leaching indicate possible improvements of nitrogen use efficiencies in European farming systems. Moreover, the applied optimization approach can be used also in a prognostic way to predict optimal timings and fertilization options (rates and splitting) upon accurate weather forecasts combined with the knowledge of modeled soil nutrient availability and plant nitrogen demand.

SPOT-Based Sub-Field Level Monitoring of Vegetation Cover Dynamics: A Case of Irrigated Croplands

Acquiring multi-temporal spatial information on vegetation condition at scales appropriate for site-specific agricultural management is often complicated by the need for meticulous field measurements. Understanding spatial/temporal crop cover heterogeneity within irrigated croplands may support sustainable land use, specifically in areas affected by land degradation due to secondary soil salinization. This study demonstrates the use of multi-temporal, high spatial resolution (10 m) SPOT-4/5 image data in an integrated change vector analysis and spectral mixture analysis (CVA-SMA) procedure. This procedure was implemented with the principal objective of mapping sub-field vegetation cover dynamics in irrigated lowland areas within the lowerlands of the Amu Darya River. CVA intensity and direction were calculated separately for the periods of 1998–2006 and 2006–2010. Cumulative change intensity and the overall directional trend were also derived for the entire observation period of 1998–2010. Results show that most of the vector changes were observed between 1998 and 2006; persistent conditions were seen within the study region during the 2006–2010 period. A decreasing vegetation cover trend was identified within 38% of arable land. Areas of decreasing vegetation cover were located principally in the irrigation system periphery where deficient water supply and low soil quality lead to substandard crop development. During the 2006–2010 timeframe, degraded crop cover conditions persisted in 37% of arable land. Vegetation cover increased in 25% of the arable land where irrigation water supply was adequate. This high sub-field crop performance spatial heterogeneity clearly indicates that current land management practices are inefficient. Such information can provide the basis for implementing and adapting irrigation applications and salt leaching techniques to site-specific conditions and thereby make a significant contribution to sustainable regional land management.

Spatial Variability of Organic Matter Dynamics in the Semi-Arid Croplands of Northern Kazakhstan

Chernozem soils have been recognized as one of the most important soils for food production and for the sink of organic matter on a global scale. Soil degradation or accelerated organic matter decomposition has been reported recently in northern Kazakhstan due to specific agricultural management such as summer fallow to increase the soil moisture for the cropping season. The objectives of the present study were 1) to evaluate the carbon-related properties of soil and plant in relation to the topography and amount of available water in upland fields, 2) to analyze their spatial variability using geostatistics and 3) to propose a rational system of management for the promotion of sustainable agriculture in this region. Field investigations were carried out in large-scale upland fields in Shortandy, northern Kazakhstan, where a crop rotation system had been developed on Typic Haplus-tolls or Southern Chernozem soils. The study field (14 km × 5 km) was divided into 70 plots (1 km × 1 km each) and, at the center of each plot, organic carbon (C) content (0–90 cm) and potentially mineralizable C content (0–15 cm) in soil, total, ear and shoot C contents in plant, and crop yield were investigated in addition to the elevation and soil water content at the beginning of the growing season. The total amount of C stored in soil (0–90 cm) was 170.9 t ha−1 with the highest C content of 39.8t ha−1 being recorded in the surface soil (0–15 cm). Potentially mineralizable C in soil (0–15 cm) amounted to 2.72 t ha−1, equivalent to 6.8% of the total C in the surface soil, suggesting that a considerable part of C in soil could be released as CO2 under favourable conditions for organic matter decomposition. Plant aboveground biomass C amounted to 1.8 t ha−1, of which 1.2 t ha−1 was returned to the field as plant residues and 0.6 t ha−1 was removed as crop (ear). Coefficients of variation in the amount of soil mineralizable C and plant properties exceeded 40%, suggesting a considerable variation in the field. Correlation analysis indicated that the elevation showed a positive relationship with the water content, soil organic C content (p < 0.01), content of potentially mineralizable C and plant yield (p < 0.05). The spatial patterns of the measured properties in the isarithmic maps showed that the content of soil organic C was the highest in the top plateau; water content, plant C content and yield were the highest in the north-facing slope area; whereas the values of all of these parameters were relatively low in the south-facing slope area. These results strongly suggest that the organic matter dynamics in the field was considerably affected by the topography and that the most favourable area for the storage of organic matter was different from that for food production. In conclusion, site-specific agricultural management based on the spatial patterns of organic matter dynamics would be a suitable option for harmonizing sustainable agricultural production with environmental conservation by reducing organic matter decomposition.

Remote Sensing for Crop Management

with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitor- ing of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and position- locating technologies, remote sensing from ground-, air-, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which empha- sizes contributions by ARS researchers, reviews the biophysi- cal basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and as- sesses the role of remote sensing in yield prediction. It con- cludes with a discussion of challenges facing remote sens- ing in the future.