Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Site‐specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (EC_a) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and EC_a. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.     

Soil Factors and their Influence on Within-field Crop Variability, Part II: Spatial Analysis and Determination of Management Zones

Spatial variation of crop yields was examined in three trial cereal fields in England from 1994 through 1997. The fields were managed with uniform inputs but there were considerable differences in the spatial patterns and magnitudes of variation between fields and seasons. Up to 50% of the yield variation was within the tramline spacing distance (20–24 m) and this appeared to relate to crop management practices rather than underlying soil factors. Longer-range variation generally increased up to field scale but was not constant between seasons. Longer-range variation was more apparent in dry years and was attributable to soil variation. Soil series differences coincided with yield differences in dry years when the soil series differences could be expected to create large differences in soil–water relationships. Soil electrical conductivity, measured by electromagnetic induction (EMI), was investigated as a surrogate for detailed soil coring. Field zones created by EMI also coincided with yield differences and zones were similar to those delineated by soil series with expected differences in soil–water relationships. The EMI observations were found to be a useful and cost-effective surrogate for representing soil variability in fields likely to create yield variations. Subdivision of fields into management zones using multi-variate K-means cluster analysis of historical yield and EMI observations formed an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies. The appropriateness of site-specific management has to be assessed annually because magnitude and pattern of variation changes from season to season.     

Spatial variability of selected soil properties and its impact on the grain yield of oats (Avena sativa L.) in small fields

Abstract

To investigate spatial variability in topsoil (0–20?cm) pH, available phosphorus (P), potassium (K), total nitrogen (N), and soil organic matter (SOM) of small fields (～2?ha), and to determine the impact of soil heterogeneity on the spatial variability of crop yield two fields were cropped with spring oats and one with winter wheat under humid-temperate conditions. In the two oat fields, some of the measured soil properties (P, K) and the grain yield varied considerably, and strong spatial trends were recorded for most of the soil traits. In the third field, soil properties showed only a moderate spatial variation, and no spatial trends were found. The spatial distribution of SOM and total N in the topsoil had some influence on the spatial pattern of the oat grain yield in the field of Gränichen; however, spatial relationships between soil chemical properties and grain yield were rather weak in our study.     

An analysis of the potential of precision farming in Northern Europe

Abstract. Precision farming is the process of adjusting husbandry practices within a field according to measured spatial variability. In this review, we explore the prospects for precision farming using the principles that underly conventional soil management and agronomy. The cost-effectiveness of precision farming is determined by the cost of defining zones within fields, the stability of zones through time, the difference in treatment between zones in terms of cost, and the responsiveness of the crop in terms of yield and quality to changes in treatment. Cost-effective precision farming is most likely where prior knowledge indicates large heterogeneity and where treatment zones can be predicted, for example from soil type or field history. Soil related factors are likely to provide the main basis for precision farming because they tend to be stable through time and influence crop performance. In particular, soil mapping may usefully indicate the moisture available for crop growth, organic matter maps may be utilized for precision application of fertilizers and soil acting herbicides, and variation in soil pH can be mapped and used as a basis for variable lime application. However, comprehensive nutrient mapping is less likely to be economic with existing techniques of chemical analysis. The value of yield mapping lies in identifying zones which are sufficiently stable to be of use in determining future practices. Maps of grain quality and nutrient content would significantly augment the value of yield maps in guiding marketing decisions and future agronomy. Interactions between soil differences and seasonal weather are large, so yield maps show considerable differences from season to season. Interpretation of such maps needs to follow a careful, informed, analytical process. Extensive and thorough field experimentation by crop scientists over many years has shown that yield variation arises as a result of a large and complex range of factors. It is highly improbable that simple explanations will be appropriate for much in-field yield variation. However, the capacity to sense yield variability within fields as opposed to between fields, where there are many confounding differences, provides an opportunity for the industry to improve its understanding of soil-based effects on crop performance. This should support its decision taking, whether through precision farming or through field-by-field agronomy. The main obstacle to the adoption of precision farming is the lack of appropriate sensors. Optimal sensor configurations that will measure the specific needs identified by end-users need to be developed. The conclusions reached in this paper probably apply to farming throughout northern Europe.     

Scientific evaluation of smallholder land use knowledge in Central Kenya

The following study was conducted to determine smallholders' land use management practices and agricultural indicators of soil quality within farmers' fields in Chuka and Gachoka divisions in Kenya's Central Highlands. Data on cropping practices and soil indicators were collected from farmers through face‐to‐face interviews and field examinations. Farmers characterised their fields into high and low fertility plots, after which soils were geo‐referenced and sampled at surface depth (0–20 cm) for subsequent physical and chemical analyses. Farmers' indicators for distinguishing productive and non‐productive fields included crop yield, crop performance and weed species. Soils that were characterised as fertile, had significantly higher chemical characteristics than the fields that were of poor quality. Fertile soils had significantly higher pH, total organic carbon, exchangeable cations and available nitrogen. Factor analysis identified four main factors that explained 76 per cent of the total variance in soil quality. The factors were connected with farmers' soil assessment indicators and main soil processes that influenced soil quality in Central Kenya. Soil fertility and crop management practices that were investigated indicated that farmers understood and consequently utilised spatial heterogeneity and temporal variability in soil quality status within their farms to maintain and enhance agricultural productivity. Copyright © 2007 John Wiley & Sons, Ltd.     

SOIL QUALITY AND SPATIAL VARIABILITY ASSESSMENT OF LAND USE EFFECTS IN A TYPIC HAPLUSTOLL

Soil management practices can have negative or positive effects on soil quality. Our objective was to assess the effect of long‐term agricultural practices by evaluating selected soil physical and chemical properties. Soil samples were collected from two depths (0 to 15 and 15 to 30 cm) within a native pasture and an adjacent agricultural field that was being used for three different crop rotations. Soil quality was quantified using aggregate stability, bulk density, soil texture and available water content as physical properties and pH, electrical conductivity, organic matter and available phosphorus as chemical properties. The farmland soils were functioning at 71 and 70 per cent of their full potential at the 0‐ to 15‐ and 15 to 30‐cm‐depth increments, respectively, whereas those from the pasture were functioning at 73 and 69 per cent, respectively. The assessment showed substantial loss in soil organic carbon following 50 years of farmland cultivation. Tillage and fertilizer applications were presumably the primary reasons for weaker spatial dependence within farmland at the 0‐ to 15‐cm depth. Grazing was postulated as the main reason for weaker spatial dependence within the pasture soils at the 15‐ to 30‐cm depth. Overall, we conclude that 50 years of cultivation has not caused soil quality to decline to a point that threatens sustainability of the agricultural fields. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluation of temporal, spatial, and tillage-induced variability for parameterization of soil infiltration

Model simulation of water and chemical transport requires information on soil hydraulic properties. Recently, independent parameterization methods have been developed to characterize soil type, tillage, temporal and spatial effects of soils. This study determines the relative magnitude of tillage-induced, temporal (yearly and seasonal), and spatial (within fields and between rows) variability in a combined analysis of soil infiltration in an agricultural field and evaluates the appropriateness of various parameterization scenarios. Infiltration measurements were obtained in the row and interrow position under plow-tilled and ridge-tilled corn (Zea mays L.) in four replicates on multiple dates in a wetter (1990) and dryer year (1991). Measurements exhibited significant temporal variability within a growing season, especially in a dry year under plow till when soil cracking resulted in higher infiltration. Position with respect to the row was the most significant source of variability under ridge till, but not under plow till. Row and interrow differences in a ridge-tilled soil are the result of dense soil and lack of disturbance in the interrow. Yearly variations and field variability were relatively low. Differences between tillage practices were primarily expressed in variable susceptibility to spatial and temporal variation. Adequate parameterization of soil infiltration on agricultural fields requires recognition of various sources of variability under different tillage management systems, weather and climatic conditions, and soil types. High intrinsic variability of soil infiltration must be accounted for through increased sampling (e.g. duplicate measurements) and the use of stochastic methods.     

中国东南地区两土链土壤中土壤性质和水稻产量的空间变异研究

In the light of an increasing demand for staple food, especially rice, in southeast China, investigations on the specific site potential expressed as the relationship between soil and crop yield parameters gain increasing importance. Soil texture and several soil chemical parameters as well as plant properties such as crop height, biomass and grain yield were investigated along two terraced catenas with contrasting soil textures cropped with wet rice. We were aiming at identifying correlative relationships between soil and crop properties. Data were analyzed both statistically and geostatistically on the basis of semivariograms. Statistical analysis indicated a significant influence of the relief position on the spatial distribution of soil texture, total carbon and total nitrogen contents. Significant correlations were found for the catena located in a sandstone area （Catena A） between rice yield and silt as well as total nitrogen content. Corresponding relationships were not detectable for paddy fields that developed from Quaternary clays （Catena B）. As suggested by the nugget to sill ratio, spatial variability of soil texture, total carbon and nitrogen was mainly controlled by intrinsic factors, which might be attributed to the erosional transport of fine soil constituents, indicating the importance of the relief position and slope in soil development even in landscapes that are terraced. The crop parameters exhibited short ranges of influence and about one third of their variability was unexplained. Comparable ranges Of selected crop and soil parameters, found only for Catena A, are indicative of close spatial interactions between rice yield and soil features. Our findings show that especially in sandstone-dominated areas, a site-specific management can contribute to an environmentally safe rice production increase.     

基于多源数据的县域冬小麦氮肥调控管理分区

为便于对县域尺度冬小麦进行精确氮肥调控,该文以安阳县冬小麦种植区为研究对象,在3S技术的支持下,利用定性和定量相结合的分析方法,分别对影响冬小麦氮肥调控的重要因子,如地形、土壤类型、土壤养分和光谱信息等多源数据,进行聚类和综合叠加分析,以确定最终的县域氮肥调控管理分区。基于土壤养分和产量的管理分区划分结果评价表明,各级分区土壤养分和产量的变异系数均有所减低,其中三级分区的21个子区为最终分区结果,其管理分区之间的土壤理化性质差异显著,有机质、碱解氮和产量变异系数均值分别降低70%、53%、66%。基于光谱数据的管理分区划分结果评价表明,不同分区内归一化植被指数(NDVI)的集中程度明显提高。分区结果可以作为冬小麦氮肥调控的决策单元,为精确农业管理提供有效途径。     

Tillage effects on soil hydraulic properties in space and time: State of the science

Soil tillage practices can affect soil hydraulic properties and processes dynamically in space and time with consequent and coupled effects on chemical movement and plant growth. This literature review addresses the quantitative effects of soil tillage and associated management (e.g., crop residues) on the temporal and spatial variability of soil hydraulic properties. Our review includes incidental management effects, such as soil compaction, and natural sources of variability, such as topography. Despite limited research on space–time predictions, many studies have addressed management effects on soil hydraulic properties and processes relevant to improved understanding of the sources of variability and their interactions in space and time. Whether examined explicitly or implicitly, the literature includes studies of interactions between treatments, such as tillage and residue management. No-tillage (NT) treatments have been compared with various tillage practices under a range of conditions with mixed results. The trend, if any, is for NT to increase macropore connectivity while generating inconsistent responses in total porosity and soil bulk density compared with conventional tillage practices. This corresponds to a general increase in ponded or near-zero tension infiltration rates and saturated hydraulic conductivities. Similarly, controlled equipment traffic may have significant effects on soil compaction and related hydraulic properties on some soils, but on others, landscape and temporal variability overwhelm wheel-track effects. Spatial and temporal variability often overshadows specific management effects, and several authors have recognized this in their analyses and interpretations. Differences in temporal variability depend on spatial locations between rows, within fields at different landscape positions, and between sites with different climates and dominant soil types. Most tillage practices have pronounced effects on soil hydraulic properties immediately following tillage application, but these effects can diminish rapidly. Long-term effects on the order of a decade or more can appear less pronounced and are sometimes impossible to distinguish from natural and unaccounted management-induced variability. New standards for experimental classification are essential for isolating and subsequently generalizing space–time responses. Accordingly, enhanced methods of field measurement and data collection combined with explicit spatio-temporal modeling and parameter estimation should provide quantitative predictions of soil hydraulic behavior due to tillage and related agricultural management.     

Developing Strategies for Spatially Variable Nitrogen Application in Cereals, Part II: Wheat

For precision agriculture to provide both economic and environmental benefits over conventional farm practice, management strategies must be developed to accommodate the spatial variability in crop performance that occurs within fields. Experiments were established in crops of wheat (Triticum aestivum) over three seasons in two fields. The aim was to evaluate a set of variable rate nitrogen strategies and examining the spatial variation in crop response to applied N. The optimum N application rate in the field with three different soil series (predominantly calcareous silty clay loam over oolitic limestone) was uniform across the field. In contrast the other field with uniform soil type (slightly calcareous brown clay loam), provided a more variable response. Estimates of yield potential, produced from either historic yield data or shoot density maps derived from airborne digital photographic images, were used to divide experimental strips into management zones. These zones were then managed according to two N application strategies. The results from the historic yield approach, which is currently the most practical commercial system, based on 3 yr of yield data, were variable with no overall yield or economic advantages. It was concluded that that this approach may not provide a suitable basis for varying N rates. The shoot density approach, however, offered considerably greater potential as it takes account of variation in the current crop. Using this approach, it was found that there was insufficient variation in the shoot density in the field with diverse soil types. However, in the field with the uniform soil type, applying additional N to areas with a low shoot population and maintaining the standard N rate to areas with an average shoot population resulted in an average strategy benefit of up to 0·46 t ha⁻¹ compared with standard farm practice. It is necessary to combine the ‘real-time’ data on relative crop structure, obtained by remote sensing with ground truth assessments and absolute benchmark values to successfully adjust N input levels to maximise yield.     

Effect of fertilizers and irrigation on multi-configuration electromagnetic induction measurements

Electromagnetic induction (EMI) data are often used to investigate spatial and temporal patterns of soil texture, soil water content and soil salinity. We hypothesized that the EMI methodology might thus also offer potential to detect agricultural legacy effects originating from fertilizer application and irrigation of different fields. Therefore, we performed EMI measurements on two long-term field experiments (LTFE) at Thyrow near Berlin (Germany) that differed in agricultural management with regard to long-term irrigation in combination with mineral (NPK and lime) and organic amendments (straw and farmyard manure). Two different rigid-boom multi-coil EMI instruments were used to measure simultaneously the apparent electrical conductivity (ECa) over nine different depth ranges to study the entire soil profile from topsoil to deep subsoil. Additionally, soil samples were taken from the different treatments to ground-truth the measurements and disentangle the nutrient application or irrigation effects from natural soil heterogeneity. The soil samples indicated a rather homogenous soil and the correlation between soil parameters or states were not significant. However, the treatments showed significant differences in measured ECa values. In general, ECa values were largest on regularly irrigated as well as on mineral and organic fertilized plots, with regular irrigation exhibiting the largest impact on EMI records even though the last application was months before the EMI measurement. Overall, this study reveals that EMI data can support the classical in situ assessment of agricultural management effects within LTFE, while offering new potentials in detecting and understanding legacy effects of agricultural management on spatial soil properties at farm level.     

Use of GIS to Delineate Site-Specific Management Zone for Precision Agriculture

Soil heterogeneity is one of the several factors that explain within-field variations in crop yielding. To overcome this problem, an experiment was conducted within the University of Ibadan to identify the zones with similar soil properties that can be managed uniformly to optimize crop yield. A total of 62 soil samples were collected from 6.5 ha of farmland based on the differences in slope, soil moisture regime, land management and cropping pattern. The spatial distributions of soil properties were mapped out using the Inverse Distance Weighing (IDW) interpolation technique and the results were subsequently reclassified into different soil management categories. The results showed that the farmland consists of four classes of total nitrogen (N), phosphorus (P) and soil bulk density (SBD); three classes of carbon (C) and volumetric moisture content (?); two classes of soil _pH; and one class of soil texture. Each of the soil management maps was subsequently overlaid on each other to produce four specific site management units (SSMUs) for vegetable cultivation. The four SSMUs consisted of highly suitable units occupying 0.143 ha, marginally suitable unit occupying 0.971 ha, moderately suitable unit occupying 0.517 ha and unsuitable unit occupying 4.9 ha of land. The SSMUs will alleviate problems of reduced yield due to over- or under-applications of agrochemicals.     

A comparison of two methods to predict the landscape-scale variation of crop yield

Landscape-scale variation is a source of information that increasingly is being taken into consideration in agricultural and environmental studies. Models that encompass and interpret this variation in fields and across contrasting management practices have the potential to improve the landscape management of agroecosystems. Our objective was to compare the results of two approaches, analysis of covariance (ANCOVA) and state-space modeling, to determine the factors affecting grain yield in three crop rotations [pea (Pisum sativum L.)–wheat (Triticum aestivum L.)–barley (Hordeum vulgare L.), canola (Brassica napus L.)–wheat–barley, and wheat–wheat–barley] at two sites in Saskatchewan, Canada. Crop rotations were established in adjacent 30 m×80 m plots arranged in a randomized complete block with five replicates. Variables that were expected to affect resource availability and pest infestations in wheat (second rotation phase) or barley (third rotation phase) were measured. Each sampling point was classified according to landscape position as either a shoulder or footslope. Landscape position was considered as a cross-classified treatment along with crop rotation, and analyzed using ANCOVA procedures. State-space modeling was conducted on a single transect connecting sampling points across all of the rotations and replicates at each site. ANCOVA frequently indicated that grain yield and other measured variables differed between landscape position across all rotations, or in a specific crop rotation. For example, grain yield, soil water content, soil N availability during the growing season, and the incidence of common root rot were higher in the footslopes than the shoulders in all of the crop rotations at one of the sites. However, the landscape position effect for grain yield was never fully explained by the landscape position effects detected for the other variables (e.g., higher soil water content in the footslopes did not correspond with higher grain yields in footslope positions at both sites). State-space modeling indicated that most of the measured variables contributed to the prediction of landscape-scale variation for grain yield in the pea–wheat rotation; whereas only leaf and root disease incidences explained landscape-scale variation in the wheat–wheat rotation. The selective omission of data indicated that state-space modeling was accounting for the varied importance of the predictors across the landscape; i.e., localized response functions. The major reason that ANCOVA did not explain landscape-scale variation of grain yield across the different crop rotations may be because it was unable to account for highly localized variation. However, there is evidence from other studies that the ANCOVA approach is appropriate when the response functions explaining grain yield do not vary significantly within the study area. This situation is most likely to occur in studies with smaller experimental areas. Future research conducted at scales reflecting ‘real world’ field conditions (i.e., study units representative of producer’s fields) should consider the use of state-space modeling or alternative statistical techniques that are designed to address and predict the complex and dynamic nature of landscape-scale processes.     

生物炭及炭基硝酸铵肥料对土壤化学性质及作物产量的影响

为了促进生物炭研究和农用,采用盆栽试验研究了两种生物炭基氮肥及相应生物炭对土壤部分化学性质、养分状况及作物产量的影响。试验结果表明：施用生物炭基氮肥可显著提高土壤有机碳含量,提高土壤pH值、阳离子交换量、土壤速效磷、速效钾和矿质态氮含量,增强土壤保肥能力,促进作物增产。生物炭对土壤化学性质和养分状况虽有一定改善作用,但作物增产效应不明显甚至减产。因此,将生物炭与肥料复合制成生物炭基肥料不但可以保持生物炭改良土壤的功能,还可促进作物生长和增产,有利于生物炭农用效益的提升。     

长江三角洲典型地区土壤有效铜和锌的时空变化及其影响因素研究

在2004年土壤调查数据的基础上,参照1982年土壤普查的资料,分析了张家港市土壤中有效铜和有效锌的时空变化趋势,并探讨了土壤性质、农业生产管理措施及环境污染等因素对它们的影响。研究结果表明:二十几年来,该地区土壤中有效铜和有效锌含量有了显著增加。在空间上,虽然土壤有效性铜和锌在水耕人为土高于北部的潮湿雏形土的格局没有改变,但在局部地区积聚的现象较为明显。这些时空变异的特征是由于二十多年来该地区农业生产管理措施的改变,工业发展等的外在因素,通过土壤性质发生改变(pH降低、有机质含量增加)而造成的。同时,工业、农业和城市活动带来的废弃物进入土壤中,也可导致土壤有效铜和锌含量增加。土壤有效铜和锌的时空演变,导致了研究区出现了铜过量的土壤,因此今后注意土壤铜过量对作物产生的危害。而土壤有效锌虽然有所增加,但大部分土壤仍处于较低或缺乏的状态,农业生产中应注意恰当施用锌肥,以提高作物产量,改善作物品质。     

Remote sensing of crop residue cover and soil tillage intensity

Management of plant litter or crop residues in agricultural fields is an important consideration for reducing soil erosion and increasing soil organic C. Current methods of quantifying crop residue cover are inadequate for characterizing the spatial variability of residue cover within fields or across large regions. Our objectives were to evaluate several spectral indices for measuring crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. Landsat Thematic Mapper (TM) and EO-1 Hyperion imaging spectrometer data were acquired over agricultural fields in central Iowa in May and June 2004. Crop residue cover was measured in corn (Zea mays L.) and soybean (Glycine max Merr.) fields using line-point transects. Spectral residue indices using Landsat TM bands were weakly related to crop residue cover. With the Hyperion data, crop residue cover was linearly related to the cellulose absorption index (CAI), which measures the relative intensity of cellulose and lignin absorption features near 2100 nm. Coefficients of determination (r²) for crop residue cover as a function of CAI were 0.85 for the May and 0.77 for the June Hyperion data. Three tillage intensity classes, corresponding to intensive (<15% residue cover), reduced (15–30% cover) and conservation (>30% cover) tillage, were correctly identified in 66–68% of fields. Classification accuracy increased to 80–82% for two classes, corresponding to conventional (intensive + reduced) and conservation tillage. By combining information on previous season's (2003) crop classification with crop residue cover after planting in 2004, an inventory of soil tillage intensity by previous crop type was generated for the whole Hyperion scene. Regional surveys of soil management practices that affect soil conservation and soil C dynamics are possible using advanced multispectral or hyperspectral imaging systems.     

An application of PCA and fuzzy C-means to delineate management zones and variability analysis of soil

Within-field variability is a well-known phenomenon and its study is at the centre of precision agriculture (PA). In this paper, site-specific spatial variability (SSSV) of apparent Electrical Conductivity (EC_a) and crop yield apart from pH, moisture, temperature and di-electric constant information was analyzed to construct spatial distribution maps. Principal component analysis (PCA) and fuzzy c-means (FCM) clustering algorithm were then performed to delineate management zones (MZs). Various performance indices such as Normalized Classification Entropy (NCE) and Fuzzy Performance Index (FPI) were calculated to determine the clustering performance. The geo-referenced sensor data was analyzed for within-field classification. Results revealed that the variables could be aggregated into MZs that characterize spatial variability in soil chemical properties and crop productivity. The resulting classified MZs showed favorable agreement between EC_a and crop yield variability pattern. This enables reduction in number of soil analysis needed to create application maps for certain cultivation operations.     

The influence of soil properties on denitrifying bacterial communities and denitrification potential in no-till production farms under contrasting management in the Argentinean Pampas

The aim of this work was to investigate the response of the structure, abundance and potential activity of denitrifiers to contrasting agricultural management in no-till production fields, across a regional scale within the highly productive Argentine northern Pampas. Treatment categories were grouped according to the sustainability of the soil management, in terms of crop rotation, fertilization, agrochemicals use and pest control, as good no-till agricultural practices (GAP) and poor no-till agricultural practices (PAP). Non-cultivated soils in each geographic location were also evaluated as reference groups.Mixed models, with sites treated as random factors, indicated that the potential denitrification activity and the size of the nirS community differed significantly between non-cultivated and PAP soils. Soil properties were separated into dynamic and inherent according to their variance components. The former had the largest part of their variances explained by agricultural management, while the latter were more affected by edaphic–climatic differences between sites. Both inherent and dynamic properties could explain the changes in potential denitrification activity, whereas changes in the abundance of denitrifiers were only related to inherent soil properties. Results from principal components analysis suggested site-specific response of most dynamic soil properties. Among the latter, only aggregate stability indices were strongly associated with potential denitrification activity after removing the geographical effect.