Agitated soil measurement method for integrated on-the-go mapping of soil pH, potassium and nitrate contents

Knowledge of spatial variability of soil attributes within an agricultural field is critical for successful site-specific crop management. Soil sensing techniques to assess this variability on-the-go are being developed as an alternative to tedious manual soil sampling and laboratory testing. The goal of this study was to evaluate an Agitated Soil Measurement (ASM) method for integrated on-the-go mapping of soil pH, soluble potassium and residual nitrate contents using ion-selective electrodes. To implement ASM, an Integrated Agitation Chamber Module (IACM) was developed and attached to a commercial soil pH mapping implement. Precision of the tested electrodes was assessed through the root mean squared error (RMSE) and ranged from 0.10 for pK to 0.22 for pNO₃ (units represent the negative base 10 logarithm of the molar concentration of specified ions). The accuracy of the electrodes was assessed by comparing test results against reference measurements conducted in a commercial soil laboratory using the linear regression method. Average accuracy error ranged from 0.11 for pK to 0.23 for pNO₃. In a field simulation test, neither precision nor accuracy errors obtained with ASM were lower than for a previously investigated Direct Soil Measurement (DSM) method, which produced precision errors ranging from 0.11 for pH to 0.22 for pNO₃ and accuracy errors ranging from 0.12 for pNO₃ to 0.20 for pH. The coefficients of determination (r²) of linear regressions between individual field simulation measurements and corresponding average reference measurements were 0.85–0.89 (pH), 0.50–0.54 (pK), and 0.14–0.32 (pNO₃). However, laboratory evaluation of the ASM method revealed substantially lower measurement errors and increased r² values when compared to the field simulation, indicating that the proposed ASM method retains the potential for improving on-the-go field mapping. Except for reduced electrode abuse and the ability to use less expensive half-cell ion-selective electrodes, physical implementation of ASM through the IACM did not bring substantial improvement over conventionally available DSM. This could be attributed to the design of the IACM and use of half-cell electrodes. Further research is necessary to improve the design of the solution-based measuring equipment and to develop an algorithm integrating on-the-go measurements with other sources of spatial data for an improved decision-making process.     

A Model for Agro-Economic Analysis of Soil pH Mapping

Core soil sampling followed by laboratory analysis is the traditional method used to map soil pH prior to variable rate application (VRA) of lime on cropland. A recently developed automated soil sampling system capable of measuring soil pH on-the-go has significantly increased sampling resolution. However, adoption of such systems must be justified economically. This paper presents a method for assessing the economic benefit from automated mapping of soil pH prior to variable rate lime application. In this work, geostatistical, agronomic, and economic methods were used to generate a comprehensive numerical model for quantitative assessment of the net return over cost of liming for different lime management strategies. The strategies included: automated pH mapping, manual grid soil sampling, and whole field sampling used in combination with either variable or fixed rate liming. The model was demonstrated using a simulated field with known average pH and semivariogram model. The analysis showed the largest benefit ($6.13ha^–1year^–1) from using VRA with automated soil pH mapping versus VRA based on 1ha (2.5acres) manual grid point sampling for the selected simulated field conditions. A sensitivity analysis demonstrated that for a wide range of field conditions and crop prices, VRA plus automated mapping promises higher relative benefits than VRA based on either manual grid point or grid cell sampling.     

Field-scale validation of an automated soil nitrate extraction and measurement system

One of the many gaps that needs to be solved by precision agriculture technologies is the availability of an economic, automated, on-the-go mapping system that can be used to obtain intensive and accurate ‘real-time’ data on the levels of nitrate nitrogen (NO₃–N) in the soil. A soil nitrate mapping system (SNMS) has been developed to provide a way to collect such data. This study was done to provide extensive field-scale validation testing of the system’s nitrate extraction and measurement sub-unit (NEMS) in two crop (wheat and carrot) production systems. Field conditions included conventional tillage (CT) versus no tillage (NT), inorganic versus organic fertilizer application, four soil groups and three points in time throughout the season. Detailed data analysis showed that: (i) the level of agreement, as measured by root mean squared error (RMSE), mean absolute error (MAE) and coefficient of efficiency (CE), between NEMS soil NO₃–N and standard laboratory soil NO₃–N measurements was excellent; (ii) at the field-scale, there was little practical difference when using either integer or real number data processing; (iii) regression equations can be used to enable field measurements of soil NO₃–N using the NEMS to be obtained with laboratory accuracy; (iv) future designs of the SNMS’s control system can continue to use cheaper integer chip technology for processing the nitrate ion-selective electrode (NO₃ ⁻–ISE) readings; and (v) future designs of the SNMS would not need a soil moisture sensor, ultimately saving on manufacturing costs of a more simple system.     

Remote sensing of soybean canopy as a tool to map high pH, calcareous soils at field scale

Soybean (Glycine max Merr.) is extensively grown in areas of the US Corn Belt where soils often range from relatively acid (pH < 6) to alkaline, calcareous. Iron availability decreases with increase in pH, consequently, soybean can suffer from iron deficiency chlorosis on high pH, calcareous areas of the field. The extent of those areas sometimes can be significant, but they often occur in complex and discontinuous patterns. The objective of the research was to explore how remote sensing of soybean canopies and GIS technologies could be used to map and quantitatively describe the extent of high pH, calcareous soils at field scale. Aerial images that consisted of visible red, green, blue, and near infrared bands were used to calculate green normalized difference vegetative index (GNDVI) and to guide plant and soil sampling at 10 fields during 2003 and 2004 growing seasons. Ten to 18 sampling areas were selected on each field to include a wide range in GNDVI values. Soil samples were analyzed for pH and calcium carbonate equivalent (CCE). Plant samples were used to estimate grain yields. Soil pH and CCE were significantly correlated with GNDVI values in eight and seven sites, respectively. A previously developed alkalinity stress index (ASI), which combines pH and CCE in one value, was significantly related to GNDVI at all 10 sites. Remote sensing of soybean canopy was shown to be a promising tool that can be used to quantitatively describe distribution of alkaline soils at field scale.     

Perceived importance of precision farming technologies in improving phosphorus and potassium efficiency in cotton production

Site-specific information technologies (IT) provide knowledge about the spatial variability within a field to improve the efficiency of inputs through variable-rate (VR) applications. Identifying factors that influence farmers’ perceptions of the importance of precision farming (PF) technologies in improving the efficiency of phosphorus (P) and potassium (K) fertilizer applications can help to determine why different groups of farmers adopt PF. Knowing these factors can be useful in targeting specific groups of farmers to adopt PF and increase fertilizer efficiency to meet crop needs and reduce P and K losses to the environment. Data were obtained from a 2001 mail survey of cotton (Gossypium hirsutum L.) farmers in six southeastern states in the United States of America. Ordered logit analysis was used to evaluate the level of importance to those who had adopted PF technologies placed on such technologies they had used to improve the efficiency of P and K applications. Results showed that such farmers found soil sampling by management zone or on a grid, and on-the-go sensing most important. Precision farmers who used mapping and remote sensing found PF technologies least important. Older precision farmers who rented larger proportions of their land and used computers for farm management were more likely than other precision farmers to place greater importance on PF technologies in improving the efficiency of P and K applications.     

Soil water status mapping and two variable-rate irrigation scenarios

Irrigation is the major user of allocated global freshwaters, and scarcity of freshwater threatens to limit global food supply and ecosystem function—hence the need for decision tools to optimize use of irrigation water. This research shows that variable alluvial soil ideally requires variable placement of water to make the best use of irrigation water during crop growth. Further savings can be made by withholding irrigation during certain growth stages. The spatial variation of soil water supplied to (1) pasture and (2) a maize crop was modelled and mapped by relating high resolution apparent electrical conductivity maps to soil available water holding capacity (AWC) at two contrasting field sites. One field site, a 156-ha pastoral farm, has soil with wide ranging AWCs (116–230 mm m⁻¹); the second field site, a 53-ha maize field, has soil with similar AWCs (161–164 mm m⁻¹). The derived AWC maps were adjusted on a daily basis using a soil water balance prediction model. In addition, real-time hourly logging of soil moisture in the maize field showed a zone where poorly drained soil remained wetter than predicted. Variable-rate irrigation (VRI) scenarios are presented and compared with uniform-rate irrigation scenarios for 3 years of climate data at these two sites. The results show that implementation of VRI would enable significant potential mean annual water saving (21.8% at Site 1; 26.3% at Site 2). Daily soil water status mapping could be used to control a variable rate irrigator.     

Spatial patterns of wilting in sugar beet as an indicator for precision irrigation

Precision irrigation requires the mapping of within-field variations of water requirement. Conventional remote sensing techniques provide estimates of water status at only shallow soil depths. The ability of a water sensitive crop, sugar beet, to act as an intermediate sensor providing an integrated measure of water status throughout its rooting depth is tested here. Archive aerial photographs and satellite imagery of Eastern England show crop patterns resulting from past periglacial processes. The patterns were found to be spatially and temporally consistent. Field sampling of soil cores to 1 m depth established that the within-field wilting zones were significantly associated with coarser or shallow soils. The stress classes, determined by classification of the digitised images, were weakly correlated with total available water (Pearson correlation r = 0.588, P < 0.05). These results suggest that wilting in sugar beet can be used as an intermediate sensor for quantifying potential soil water availability within the root zone. Within-field stress maps generated in 1 year could be applied as a strategic tool allowing precision irrigation to be applied to high-value crops in following years, helping to make more sustainable use of water resources.     

Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Accurate characterization of soil properties across a field can be difficult, especially when compounded with the diverse landscapes used for pastureland. Indirect methods of data collection have the advantage of being rapid, noninvasive, and dense; they may improve mapping accuracy of selected soil parameters. The objective of this study was to determine if the use of soil electrical conductivity (EC) as a covariate improved mapping accuracy of five soil variables across four sampling schemes and two sampling densities in a central Iowa, USA pasture. In this study, cokriging methods were compared to kriging methods for the measured soil properties of soil pH, available P and K, organic matter and moisture. Maps resulting from cokriging each of the soil variables with soil EC exhibited more local detail than the kriged maps of each soil variable. A small, but inconsistent, improvement occurred in kriging variance and prediction accuracy of non-sampled sites when cokriging was implemented. The improvement was generally greater for soil variables more highly correlated with soil EC. This work indicates that cokriging of EC with less densely and invasively collected soil parameters of P, K, pH, organic matter (OM) and moisture does not consistently and substantially improve the characterization accuracy of pasture soil variability.     

Mobile TDR for geo-referenced measurement of soil water content and electrical conductivity

The development of site-specific crop management is constrained by the availability of sensors for monitoring important soil and crop related conditions. A mobile time-domain reflectometry (TDR) unit for geo-referenced soil measurements has been developed and used for detailed mapping of soil water content and electrical conductivity within two research fields. Measurements made during the early or late season, when soil moisture levels are close to field capacity, are related to the amount of plant available water and soil texture. Combined measurements of water content and electrical conductivity are closely related to the clay and silt fractions of a variable field. The application to early season field mapping of water content, electrical conductivity and clay content is presented. The water and clay content maps are to be used for automated delineation of field management units. Based on a spatial analysis of the soil water measurements, recommendations are made with respect to sampling strategies. Depending on the variability of a given area, between 15 and 30 ha can be mapped with respect to soil moisture and electrical conductivity with sufficient detail within 8 h.     

Efficacy of Grid and Zone Soil Sampling Approaches for Site-Specific Assessment of Phosphorus,Potassium, pH,and Organic Matter

Within-field variability of plant-available nutrients often results in different fertilizer requirements across a field. There is uncertainty concerning the efficacy of alternative sampling strategies suitable for site-specific management. This study compared various soil sampling approaches for P, K, pH, and organic matter (OM) in eight agricultural fields. Soil samples were collected using an intensive 0.2-ha grid-point procedure, and were used to compare less intensive sampling approaches. The approaches were based on 1.2–1.6-ha grid cells (Grid), soil series of digitized soil survey maps (SSM), soil series of detailed soil survey (1:12,000 scale) maps, elevation zones, and management zones based on various information layers (ZS). The approaches varied in reducing the within-unit soil-test variability and maximizing mean soil-test values across sampling units, but none was superior across all fields and nutrients. All approaches were less efficient for P and K than for pH or OM. The Grid and ZS approach were the most effective across all nutrients and fields. However, the Grid approach was more effective for P, the Grid and ZS approaches were better for K and pH, and the SSM and ZS approaches were better for OM. The ZS approach often resulted in fewer sampling zones than the Grid approach, which implies lower soil testing costs for producers, but required more knowledge and subjective judgement than a Grid approach to adapt it to field-specific conditions.     

The impact of various sprinkler irrigation patterns on spatial soil moisture variation in Vertisols

The objective of this research was to assess the effect of soil cracks on soil moisture distribution under various sprinkler irrigation applications and to identify the optimal irrigation strategy that enhances soil moisture distribution and reduces water drainage for the upper soil layer 0–250 mm. The assessment was made for six irrigation events: the first two were for 10 and 46 mm water applications using a hand shift-set sprinkler system. The second set was for 43 and 19 mm water applications using the lateral move system with fixed sprayer heads and the third pair of events were for 43 and 32 mm water applications using the lateral move system with rotating sprinklers. The experiments were conducted on two adjacent fields at the University of Queensland, Gatton, Australia. Each field was divided into 2 m × 2 m grids that covered 62 sampling locations. For each event, the initial soil moisture content (SMC) was measured at each sampling location before irrigation. After irrigation, catch can readings were recorded for each sampling location. After 12 h overnight, the second set of soil moisture measurements was taken at each location. The area1 distribution of SMC for the studied applications was quantified. An attempt was made to identify the relationship between the applied water uniformity using catch cans and the soil moisture uniformity using gravimetric water content measurements. The study also took into consideration variables that could affect the soil physical and hydrological properties including the field slope, the soil texture, the infiltration rate, the salt content and the soil organic matter content of the two fields. Since the soils were cracking clay Vertisols, further analyses were conducted on the crack dynamics, size and distribution using image analysis techniques. The research findings demonstrated that the cracks were the main contributors to water drainage below 250 mm soil depth due to the micro-run off from the crust surface to the cracks. The cracks ranged from a few millimeters to more than 40 mm in width. It was observed that the cracks which were wider than 15 mm remained open after irrigation for the specified application rates. Improving the irrigation system application uniformity did not always result in higher uniformity of the surface SMC (0–250 mm). The event that best enhanced soil moisture distribution and thus improved soil moisture recharging was observed after the sixth irrigation event when the field received 32 mm water application. The soil was at a relatively high initial SMC of 25%, (which represented 43.3% of the plant available water range) and the sprinkler water uniformity was rather high above 87% Christiansen coefficient of uniformity (CUc). At this SMC, the extent of soil cracking is limited.     

Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping

The joint use of satellite imagery and digital soil maps derived from soil sampling is investigated in the present paper with the goal of proposing site-specific management units (SSMU) within a commercial field plot. Very high resolution Quickbird imagery has been used to derive leaf area index (LAI) maps in maize canopies in two different years. Soil properties maps were obtained from the interpolation of ion concentrations (Na, Mg, Ca, K and P) and texture determined in soil samples and also from automatic readings of electromagnetic induction (EMI) readings taken with a mobile sensor.Links between the image-derived LAI and soil properties were established, making it possible to differentiate units within fields subject to abiotic stress associated with soil sodicity, a small water-holding capacity or flooding constraints. In accordance with the previous findings, the delineation of SSMUs is proposed, describing those field areas susceptible of variable-rate management for agricultural inputs such as water or fertilizing, or soil limitation correctors such as gypsum application in the case of sodicity problems. This demonstrates the suitability of spatial information technologies such as remote sensing and digital soil mapping in the context of precision agriculture.     

The management and planning of citrus orchards at a regional scale with GIS

Citrus growing is regarded as an important cash crop in some regions of China. A geographic information system (GIS) was used to investigate the growing conditions of citrus orchards in Chongqing, China. Digital maps on topography, land use, soil types and climate were obtained and a digital elevation model (DEM) was produced at a scale of 1:10 000 using a GIS. A total of 50 representative orchards (2032 ha) were examined and extensive investigation were carried out in the summer of 2007. Topographic characteristics of the orchards studied were determined using the DEM. About 53% of the total area covered by the orchards has slopes of 8–25° and 4.4% has slopes steeper than 35°. The orchards were dominantly on south-facing slopes (42%). About 80% of the orchards were within 200–400 m in altitude. The orchards were mainly on soil developed on purple shale and limestone (86 and 14% of the total area, respectively). About 42% of the area has soil with a pH of less than 5.5. The majority of the study area (60%) has soil with organic matter contents of 1–2%. General guidelines for sustainable citrus production are proposed based on the topography and soil properties of the citrus orchards. The result of regional planning indicates that about one-third of the total area of Chongqing is suitable for citrus growth (2.68 × 10⁶ ha). A GIS-based database management system provides a new perspective on the management and planning of citrus orchards in Chongqing.     

Frequency Analysis of Yield for Delineating Yield Response Zones

The yield in any given field or management zone is a product of interaction between many soil properties and production inputs. Therefore, multi-year yield maps may give better insight into determining potential management zones. This research was conducted to develop a methodology to delineate yield response zones by using two-state frequency analysis conducted on yield maps for 3 years on two commercial corn fields near Wiggins, Colorado. A zone was identified by the number of years that yield was equal and greater than the average yield in a given year. Classes producing statistically similar yield were combined resulting in three potential yield zones. Results indicated that the variability of yield over time and space could successfully be assessed at the same time without the drawbacks of averaging data from different years. Frequency analysis of multi-year yield data could be an effective way to establish yield response zones. Seventeen percent of the field #1 consistently produced lower yield than the mean while 43 of the field produced yield over the mean. Corresponding values for field #2 were 6% and 42%.The remainder of the fields produced fluctuating yields between years. These spatially and temporally sound yield response maps could be used to identify the yield-limiting factors in zones where yield is either low or fluctuating. Yield response maps could also be helpful to delineate potential management zones with the help of resource zones such as electrical conductivity and soil maps, along with the directed soil sampling results.     

Soil Sampling Techniques for Alabama,USA Grain Fields

Characterizing the spatial variability of nutrients facilitates precision soil sampling. Questions exist regarding the best technique for directed soil sampling based on a priori knowledge of soil and crop patterns. The objective of this study was to evaluate zone delineation techniques for Alabama grain fields to determine which method best minimized the soil test variability. Site one (25.8 ha) and site three (20.0 ha) were located in the Tennessee Valley region, and site two (24.2 ha) was located in the Coastal Plain region of Alabama. Tennessee Valley soils ranged from well drained Rhodic and Typic Paleudults to somewhat poorly drained Aquic Paleudults and Fluventic Dystrudepts. Coastal Plain soils ranged from coarse-loamy Rhodic Kandiudults to loamy Arenic Kandiudults. Soils were sampled by grid soil sampling methods (grid sizes of 0.40 ha and 1 ha) consisting of: (1) twenty composited cores collected randomly throughout each grid (grid-cell sampling) and, (2) six composited cores collected randomly from a 3×3m area at the center of each grid (grid-point sampling). Zones were established from (1) an Order 1 Soil Survey, (2) corn (Zea mays L.) yield maps, and (3) airborne remote sensing images. All soil properties were moderately to strongly spatially dependent as per semivariogram analyses. Differences in grid-point and grid-cell soil test values suggested grid-point sampling does not accurately represent grid values. Zones created by soil survey, yield data, and remote sensing images displayed lower coefficient of variations (%CV) for soil test values than overall field values, suggesting these techniques group soil test variability. However, few differences were observed between the three zone delineation techniques. Results suggest directed sampling using zone delineation techniques outlined in this paper would result in more efficient soil sampling for these Alabama grain fields.     

Crop water stress mapping for site-specific irrigation by thermal imagery and artificial reference surfaces

Variable-rate irrigation by machines or solid set systems has become technically feasible, however mapping crop water status is necessary to match irrigation quantities to site-specific crop water demands. Remote thermal sensing can provide such maps in sufficient detail and in a timely way. In a set of aerial and ground scans at the Hula Valley, Israel, digital crop water stress maps were generated using geo-referenced high-resolution thermal imagery and artificial reference surfaces. Canopy-related pixels were separated from those of the soil by upper and lower thresholds related to air temperature, and canopy temperatures were calculated from the coldest 33% of the pixel histogram. Artificial surfaces that had been wetted provided reference temperatures for the crop water stress index (CWSI) normalized to ambient conditions. Leaf water potentials of cotton were related linearly to CWSI values with R ² = 0.816. Maps of crop stress level generated from aerial scans of cotton, process tomatoes and peanut fields corresponded well with both ground-based observations by the farm operators and irrigation history. Numeric quantification of stress levels was provided to support decisions to divide fields into sections for spatially variable irrigation scheduling.     

A Two-Factor Empirical Deterministic Response Surface Calibration Model for Site-Specific Predictions of Lime Requirement

This paper describes the development of an empirical deterministic two-factor response surface model for the Woodruff lime-requirement buffer (WRF). The model may be used to produce variable-rate lime requirement maps, or to predict lime requirements in real-time. Hence it may be suitable as a component of a decision support system (DSS) for the site-specific management of acid soil. The models' predictions were compared to those of a one-factor response surface, and those of a linear regression. The models tested were validated against soil-CaCO₃ incubations using a statistical jackknifing procedure for error and bias estimations. The Akaike Information Criterion (AIC) was used to ascertain the best model in terms of goodness of fit and parsimony. The two-factor response surface model produced the best lime requirement estimates, followed by the single-factor model, then the conventional linear regression. The advantages of the response surface models are their improved prediction accuracy, and their flexibility in the choice of any target pH (from pH 5.5 to 7) without the need for excessive calibrations. The uncertainty of the model was assessed using data from an agricultural field in Kelso, New South Wales, Australia. Block kriged maps of soil pH measured in 0.01 M CaCl₂ (pH_CaCl2), WRF buffer pH (pH_buffer) and lime requirements to a target pH of 7 were produced, to compare their spatial distributions. Finally the economic and agronomic benefits of site-specific liming were considered.     

Impact of Residual Soil Nitrate on In-Season Nitrogen Applications to Irrigated Corn Based on Remotely Sensed Assessments of Crop Nitrogen Status

Spatial and temporal variability of soil nitrogen (N) supply together with temporal variability of plant N demand make conventional N management difficult. This study was conducted to determine the impact of residual soil nitrate-N (NO₃-N) on ground-based remote sensing management of in-season N fertilizer applications for commercial center-pivot irrigated corn (Zea mays L.) in northeast Colorado. Wedge-shaped areas were established to facilitate fertigation with the center pivot in two areas of the field that had significantly different amounts of residual soil NO₃-N in the soil profile. One in-season fertigation (48 kg N ha⁻¹) was required in the Bijou loamy sand soil with high residual NO₃-N versus three in-season fertigations totaling 102 kg N ha⁻¹ in the Valentine fine sand soil with low residual NO₃-N. The farmer applied five fertigations to the field between the wedges for a total in-season N application of 214 kg N ha⁻¹. Nitrogen input was reduced by 78% and 52%, respectively, in these two areas compared to the farmer’s traditional practice without any reductions in corn yield. The ground-based remote sensing management of in-season applied N increased N use efficiency and significantly reduced residual soil NO₃-N (0–1.5 m depth) in the loamy sand soil area. Applying fertilizer N as needed by the crop and where needed in a field may reduce N inputs compared to traditional farmer accepted practices and improve in-season N management.     

Using low-cost geophysical survey to map soil properties and delineate management zones on grazed permanent pastures

Usually, soils utilised for livestock production have similar high spatial variability as those for agricultural or forest use. As a consequence, it is necessary to determine the spatial patterns of the main soil properties as the first stage to implement site-specific management. However, this has to be performed using an inexpensive technique because the profitability in these types of farm are very low, so owners need a cheap, effective, and reliable method to know which zones have similar production potential. Using soil apparent electrical conductivity (ECa) measurements, obtained with a contact sensor at many locations, as the basis to perform a directed soil sampling, 10 samples were taken at two depths (0–0.25 m and 0.25–0.50 m) in a 2.3 ha field in Évora (southern Portugal). Firstly, relationships between ECa and many soil properties were analysed using regression analysis. Six soil properties (clay, silt, fine sand, soil moisture content, pH, and cation exchange capacity) were significantly correlated with ECa. Consequently, spatial distributions of these variables were visualised using map algebra techniques. Later, a fuzzy clustering algorithm was utilised to delineate management zones, resulting in two subfields to be managed separately. Finally, a principal component analysis was conducted to analyse the influence of the soil properties and elevation on the soil variability. It was determined that elevation and clay were the most important contributing properties. Therefore, these can be regarded as key latent variables in this soil. Results showed that low-cost data based on ECa surveys can be used to implement site-specific management in soils with permanent pastures, such as those in the montado or dehesa ecosystems, in the southwest of the Iberian Peninsula.