Determining placement criteria of moisture sensors through temporal stability analysis of soil water contents for a variable rate irrigation system

Developing placement criteria for soil moisture sensors is crucial in increasing the practical functionality of a variable rate irrigation (VRI) system. In this field study, the temporal stability pattern of soil water content was compared between VRI and uniform rate irrigation (URI) treatments during growing seasons of winter wheat and summer maize to determine the placement criteria of soil water sensors. The 1.64-ha experimental site located in a highly variable alluvial flood plain was divided into four management zones according to the available water holding capacity ranging from 152 to 205 mm within the 0.6 m soil profile. In each zone, two sub-zones were created to represent VRI and URI treatments. A temporal stability analysis of soil moisture was conducted by regularly measuring soil water contents at 62 locations in the field during the growing seasons. Results showed that the VRI management changed the overall similarity of soil moisture spatial patterns when crop water consumption was provided mainly by irrigation water rather than precipitation. In each management zone, every measuring position was a time-stable location with respect to the mean soil water content. Significant linear regressions were detected between the mean clay percentile in each management zone and the clay percentile representing the mean soil water content sites, and a nearly equivalent value of fitted equation coefficient was obtained for winter wheat (1.15) and summer maize (1.19). These results demonstrated that the temporal stability of soil water content spatial patterns still existed in each management zone with the VRI management, and the clay percentile supplied a priori identification for placement of soil moisture sensors.     

Determining the within-field yield variability from seasonally changing soil conditions

Crop yield variations are strongly influenced by the spatial and temporal availabilities of water and nitrogen in the soil during the crop growth season. To estimate the quantities and distributions of water and nitrogen within a given soil, process-oriented soil models have often been used. These models require detailed information about the soil characteristics and profile architecture (e.g., soil depth, clay content, bulk density, field capacity and wilting point), but high resolution information about these soil properties, both vertically and laterally, is difficult to obtain through conventional approaches. However, on-the-go electrical resistivity tomography (ERT) measurements of the soil and data inversion tools have recently improved the lateral resolutions of the vertically distributed measurable information. Using these techniques, nearly 19,000 virtual soil profiles with defined layer depths were successfully created for a 30 ha silty cropped soil over loamy and sandy substrates in Central Germany, which were used to initialise the CArbon and Nitrogen DYnamics (CANDY) model. The soil clay content was derived from the electrical resistivity (ER) and the collected soil samples using a simple linear regression approach (the mean R² of clay = 0.39). The additional required structural and hydrological properties were derived from pedotransfer functions. The modelling results, derived soil texture distributions and original ER data were compared with the spatial winter wheat yield distribution in a relatively dry year using regression and boundary line analysis. The yield variation was best explained by the simulated soil water content (R² = 0.18) during the grain filling and was additionally validated by the measured soil water content with a root mean square error (RMSE) of 7.5 Vol%.     

Management zones delineation using fuzzy clustering techniques in grapevines

Precision viticulture aims at managing vineyards at a sub-field scale according to the real needs of each part of the field. The current study focused on delineating management zones using fuzzy clustering techniques and developing a simplified approach for the comparison of zone maps. The study was carried out in a 1.0 ha commercial vineyard in Central Greece during 2009 and 2010. Variation of soil properties across the field was initially measured by means of electrical conductivity, soil depth and topography. To estimate grapevine canopy properties, NDVI was measured at different stages during the vine growth cycle. Yield and grape composition (must sugar content and total acidity) mapping was carried out at harvest. Soil properties, yield and grape composition parameters showed high spatial variability. All measured data were transformed on a 48-cell grid (10 × 20 m) and maps of two management zones were produced using the MZA software. Pixel-by-pixel comparison between maps of electrical conductivity, elevation, slope, soil depth and NDVI with yield and grape composition maps, set as reference parameters, allowed for the calculation of the degree of agreement, i.e. the percentage of pixels belonging to the same zone. The degree of agreement was used to select the best-suited parameters for final management zones delineation. For the year 2009 soil depth, early and mid season NDVI were used for yield-based management zones while for quality-based management zones ECa, early and mid season NDVI were utilized. For the year 2010 ECa, elevation and NDVI acquired during flowering and veraison were used for the delineation of yield-based management zones while for quality-based management zones ECa and NDVI acquired during flowering and harvest were utilized. Results presented here could be the basis for simple management zone delineation and subsequent improved vineyard management.     

Apparent electrical conductivity in dry versus wet soil conditions in a shallow soil

The general objective of this study was to evaluate the stability of patterns of apparent soil electrical conductivity (EC_a) in dry versus wet soil conditions in a shallow soil typically used for pastures in Mediterranean conditions of the southern region of Portugal. A 6 ha experimental field of permanent bio-diverse pasture was divided into 76 squares of 28 × 28 m. The soil electrical conductivity was measured using a Dualem 1S sensor under dry conditions (June 2007) and under wet conditions during the rainy season (March 2010). Soil samples, geo-referenced with GPS, were collected in a depth range of 0–0.30 m. The soil was characterized in terms of bedrock depth, moisture content, texture, pH, organic matter content, and macronutrients (nitrogen, phosphorus, and potassium). Pasture samples, also geo-referenced with GPS, were collected to measure the pasture dry matter yield. The statistical analysis of apparent electrical conductivity between dry and wet soil conditions resulted in a linear significant correlation coefficient (R = 0.88). The results also showed a significant correlation between apparent electrical conductivity and the relative field elevation (R = ?0.64 and R = ?0.66), the pasture dry matter yield (R = 0.42 and R = 0.48), the bedrock depth (R = 0.40 and R = 0.27), the pH (R = 0.50 and R = 0.49), the silt (R = 0.27 and R = 0.38) and soil moisture content (R = 0.48 and R = 0.45), in dry and wet conditions, respectively. A multi-variate regression was carried out using the following soil parameters that showed significant correlation with EC_a and that did not present multi-collinearity: pH, bedrock depth, silt and moisture content. The results showed, in dry and wet conditions, that the analysis was significant (R = 0.75 and R = 0.84, respectively). Overall, these results indicate the temporal stability of EC_a patterns under different soil moisture contents, which is relevant with respect to the time when a field should be surveyed and is important for using the electrical conductivity sensor, as a decision support tool for management zones in precision agriculture.     

Spatial variability and temporal stability of apparent soil electrical conductivity in a Mediterranean pasture

The general objectives of this study were to evaluate (i) the specificity of the spatial and temporal dynamics of apparent soil electrical conductivity (EC_a) measured by a electromagnetic induction (EMI) sensor, over 7 years, in variable conditions (of soil moisture content (SMC), soil vegetation cover and grazing management) and, consequently, (ii) the potential for implementing site-specific management (SSM). The DUALEM 1S sensor was used to measure the EC_a in a 6 ha pasture experimental field four times between June 2007 and February of 2013. Soil spatial variability was characterized by 76 samples, geo-referenced with the global positioning system (GPS). The soil was characterized in terms of texture, moisture content, pH, organic matter content, nitrogen, phosphorus and potassium. This study shows a significant temporal stability of the EC_a patterns under several conditions, behavior that is an excellent indicator of reliability of this tool to survey spatial soil variability and to delineate potential site-specific management zones (SSMZ). Significant correlations were obtained in this work between the EC_a and relative field elevation, pH, silt and soil moisture content. These results open perspectives for using the EMI sensor as an indicator of SMC in irrigation management and of needs of limestone correction in Mediterranean pastures. However, it is interesting to extend the findings to other types of soil to verify the origin of the lack of correlation between the EC_a data measured by DUALEM sensor and properties such as the clay, organic matter or phosphorus soil content, fundamental parameters for establishment of pasture SSM projects.     

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.     

Apparent electrical conductivity measurements in an olive orchard under wet and dry soil conditions: significance for clay and soil water content mapping

Mediterranean olive trees traditionally grow under rainfed conditions, on poor soils with steep slopes. Rainfall is mainly concentrated during autumn and winter and is characterized by intense rain pulses, separated by dry periods. The use of electromagnetic induction (EMI) techniques in these olive orchards might be questioned since EMI surveys are generally recommended to be performed under moist soil conditions. A 6.7 ha olive orchard was surveyed for EMI-based apparent electrical conductivity (ECa), both under wet and dry soil conditions. In addition, 48 soil samples were analyzed for soil texture and for soil water content (SWC) under both soil conditions. The relationships between ECa, soil texture and SWC, under both soil conditions were evaluated. Despite the significantly larger ECa values measured during the wet survey as compared to the dry survey, a similar spatial correlation structure was found, indicating temporally stable ECa patterns. Significant correlations (r) were found between both surveys for ECa (r = 0.67) and for SWC (r = 0.63). The correlation between SWC and clay content exceeded 0.60 for both surveys, and the correlation between ECa and clay content was twice as high under wet soil conditions as compared to dry soil. In both situations, the ECa surveys revealed the same patterns of soil texture, indicating that moist soil conditions are not an absolute prerequisite for the use of EMI to map the spatial variability of these soil properties. Nonetheless, measuring the ECa under different moisture conditions can provide additional information about soil moisture dynamics.     

北京地区3种典型质地土壤水分变化规律

[目的]研究北京地区不同质地土壤水分变化规律。[方法]选取北京地区3种典型质地土壤观测站近5年的土壤水分观测资料,对土壤水分的年际变化和季节变化特征进行分析。[结果]黏土平均土壤含水率最大,壤土次之,砂壤土最小;3种质地土壤水分的季节变化均可分为4个时期,即初春短暂增墒期、春季失墒期、雨季增墒期和秋季失墒期;在雨季,砂壤土各层土壤水分随着降雨和蒸发而迅速变化,且变幅较大,黏土的变化最为平稳,但在少雨期黏土变化幅度较大。[结论]不同质地土壤水分在非冻结期内的季节变化特征总体相似,而随降水量变化的幅度有所差异。     

土壤剖面基础性质差异对农田水氮过程和作物产量的影响

【目的】华北平原地区是中国最重要的冬小麦和夏玉米生产基地,不同农田土壤基础性质差异是造成该地区农田生产力空间变异的基本原因。通过研究该地区冲积始成土冬小麦-夏玉米轮作农田土壤剖面性质对水氮过程以及作物产量形成的影响,以期为该地区高产农田的水氮利用与管理提供参考。【方法】选取位于山东省泰安市研究区3块具有不同土壤基础性质且产量存在显著性差异的农田,进行3年田间试验,测定土壤剖面的土壤基本性质,具体包括机械组成、饱和导水率、田间持水量、永久萎蔫点、有机碳、全氮;监测土壤剖面0-160 cm的水分和硝态氮的动态变化以及作物生物量、叶面积指数和产量等。运用根区水质模型（RZWQM）对各农田的水氮过程进行模拟计算。【结果】RZWQM模型在整体上可以很好地模拟2009年10月至2012年9月3年不同基础土壤性质农田水分、无机氮、作物产量、地上部生物量和叶面积动态特征,并计算各农田水氮平衡项。各农田土壤基础性质差异对水氮过程及产量形成的影响具体为：高产农田0-160 cm剖面的最大有效贮水量为223 mm,分别高出中产和低产农田28和56 mm,同时30 cm深度以下土层具有相对较低的饱和导水率。该基础性质差异使得高产农田年均水分损失（地表径流+深层渗漏）仅为150.3 mm,分别低于中产和低产农田5.7和26.4 mm,从而使高产农田作物受到相对低的水分胁迫。高产农田土壤表层土壤有机碳含量较中低产田高,而碳氮比则较低,使得高产农田具有更高的净矿化氮量（较中产和低产农田高52.0和82.6 kg·hm^-2）,且较低的氮损失（氨挥发+氮淋洗+反硝化作用）,较中产和低产农田分别少6.9和10.9 kg·hm^-2。高产农田的水分利用效率（WUE）为2.32 kg·m^-3,分别较中产和低产农田高12.1%和6.8%,这是因为高产农田受到较低的氮素胁迫。在本研究区不同土壤基础性质农田的氮素利用效率（NUE）差异不显著。【结论】在华北平原冬小麦-夏玉米轮作区,理想的土体构型能够存储更多的有效水,高土壤有机碳含量和低的碳氮比能矿化出更多的无机氮,保障了充足的水氮供应,减缓作物受到的水氮胁迫,从而获得高产。     

粘粒含量对砂壤土水分常数的影响

土壤水分常数是评价土壤改良效果的主要指标。文章通过研究不同掺粘量对砂壤土水分常数的影响及机理分析。旨在为内蒙古旱作农业砂壤土改良、扩蓄、提高生产力提供有力的科技支撑和理论依据。研究表明：砂壤土掺粘后土壤的饱和导水率明显下降,当掺粘量为5％时,饱和导水率降低约为砂壤土的36％;田间持水量、毛管持水量、饱和含水量和萎蔫含水量随着粘粒含量的增加而增加;土壤有效含水率随着掺粘量的增加呈现出先增加后降低的趋势。     

A flexible approach to managing variability in grain yield and nitrate leaching at within-field to farm scales

We up-scaled the APSIM simulation model of crop growth, water and nitrogen dynamics to interpret and respond to spatial and temporal variations in soil, season and crop performance and improve yield and decrease nitrate leaching. Grain yields, drainage below the maximum root depth and nitrate leaching are strongly governed by interaction of plant available soil water storage capacity (PAWC), seasonal rainfall and nitrogen supply in the water-limited Mediterranean-type environment of Western Australia (WA). APSIM simulates the interaction of these key system parameters and the robustness of its simulations has been rigorously tested with the results of several field experiments covering a range of soil types and seasonal conditions in WA. We used yield maps, soil and weather data for farms at two locations in WA to determine spatial and temporal patterns of grain yield, drainage below the maximum root depth and nitrate leaching under a range of weather, soil and nitrogen management scenarios. On one farm, we up-scaled APSIM simulations across the whole farm using local weather and fertiliser use data and the average PAWC values of soil type polygons. On a 70 ha field on another farm, we used a linear regression of apparent soil electrical conductivity (EC_a) measured by EM38 against PAWC to transform an EC_a map of the field into a high resolution (5 m grid) PAWC map. We then used regressions of simulated yields, drainage below the maximum root depth and nitrate leaching on PAWC to upscale the APSIM simulations for a range of weather and fertiliser management scenarios. This continuous mapping approach overcame the weakness of the soil polygons approach, which assumed uniformity in soil properties and processes within soil type polygons. It identified areas at greatest financial and environmental risks across the field, which required focused management and simulated their response to management interventions. Splitting nitrogen applications increased simulated wheat yields at all sites across the field and decreased nitrate leaching particularly where the water storage capacity of the soil was small. Low water storage capacity resulted in both low wheat yields and large leaching loss. Another management option to decrease leaching may be to grow perennial vegetation that uses more water and loses less by drainage.Paper from the 5th European Conference on Precision Agriculture (5ECPA), Uppsala, Sweden, 2005     

Analysis of soil water availability by integrating spatial and temporal sensor-based data

An efficient irrigation system should meet crop demands for water. A limited water supply may result in reductions in yield, while excess irrigation is a waste of resources. To investigate water availability throughout the growing season, on-the-go sensing technologies (field elevation and apparent electrical conductivity) were used to analyze the spatial variability of soil relevant to its water-holding capacity. High-density data layers were used to identify strategic sites to monitor changes in plant-available water over time. To illustrate this approach, nine locations in a 37-ha agricultural field were selected for monitoring the soil matric potential and temperature at four depths (18, 48, 79 and 109 cm) using wireless technology. Using a linear regression approach, a field-specific model was developed that quantified plant-available water at every field location and at specific points in time. Further analysis was used to quantify the percentage of the field that undergoes a potential shortage in water supply. These results could be used to optimize irrigation scheduling and to assess the potential for variable-rate irrigation.     

不同土地利用方式对土壤含水量、pH值及电导率的影响

以林芝地区八一镇5种植被类型下土壤为研究对象,研究其土壤剖面(0—50 cm)的含水量、pH值及电导率分布特征。结果表明,土地利用方式影响了土壤含水量、pH值和电导率在垂直剖面上的空间分布,尤其以表层(0—20 cm)土壤含水量和电导率受影响最为明显。草地土壤含水量表现出明显的“表聚”效应,即随着土层加深,含水量呈降低趋势。在区域空间上,土壤含水量和电导率具有较强的空间变异性,而土壤pH值的空间变异性较小。随着土层加深,各层土壤含水量和电导率之间的变异性逐渐减小。     

Management Zones Classified With Respect to Drought and Waterlogging

Within-field variations in potential grain yield may be due to variations in plant available soil water. Different water holding capacities affect yield differently in different years depending on weather. By estimating plant-water availability in different weathers, scenarios could be created of how yield potential and thereby fertilizer demand may vary within fields. To test this, measured cereal grain yields from a dry, a wet and an intermediate year were compared with different soil moisture related variables in a Swedish arable field consisting of clayey and sandy areas. Soil water budget calculations based on weather data and maximum plant available water (PAW), estimated from soil type and rooting data, were used to assess drought. A reasonable correlation between estimated and measured soil moisture was achieved. In the dry year, drought days explained differences in yield between the clayey and the sandy soil, but yield was better explained directly by maximum PAW, elevation, clay content and soil electrical conductivity (SEC). Yield correlated significantly with SEC and elevation within the sandy soil in the dry year and within the clayey soil in the wet year, probably due to water and nitrogen limitation respectively. Dense SEC, elevation and yield data were therefore used to divide the field into management zones representing different risk levels for drought and waterlogging. These could be used as a decision support tool for site-specific N fertilization, since both drought and waterlogging affect N fertilization demand.     

A Geostatistical Analysis of the Spatial Variation of Soil Mineral Nitrogen and Potentially Available Nitrogen Within an Arable Field

The technology for site-specific applications of nitrogen (N) fertilizer has exposed a gap in our knowledge about the spatial variation of soil mineral N, and that which will become available during the growing season within arable fields. Spring mineral N and potentially available N were measured in an arable field together with gravimetric water content, loss on ignition, crop yield, percentages of sand, silt, and clay, and elevation to describe their spatial variation geostatistically. The areas with a larger clay content had larger values of mineral N, potentially available N, loss on ignition and gravimetric water content, and the converse was true for the areas with more sandy soil. The results suggest that the spatial relations between mineral N and loss on ignition, gravimetric water content, soil texture, elevation and crop yield, and between potentially available N and loss on ignition and silt content could be used to indicate their spatial patterns. Variable-rate nitrogen fertilizer application would be feasible in this field because of the spatial structure and the magnitude of variation of mineral N and potentially available N.     

基于物联网和LSTM的柑橘园土壤含水量和电导率预测模型

目的 构建柑橘果园环境信息物联网实时采集系统,建立基于物联网和长短期记忆(LSTM)的柑橘园土壤含量和电导率预测模型,为果园灌溉施肥管理、效果预测评估提供参考依据。方法 利用土壤温度、含水量、电导率三合一传感器,在柑橘果园中设置5个节点和1个气象站,通过ZigBee短距离无线通信和GPRS远距离无线传输,将果园气象数据和土壤墒情数据传输至远程服务器。利用LSTM模型建立气象数据与土壤含水量和电导率的预测模型,计算均方根误差(RMSE)和决定系数(R²)以进行性能评估。结果 物联网系统能够实现远程传输柑橘果园环境数据,建立了基于LSTM和广义回归神经网络(GRNN)的土壤含水量和电导率预测模型,模型在5个节点的数据集的训练结果分别为：LSTM模型训练的土壤含水量和电导率的RMSE范围分别为6.74~8.65和6.68~8.50,GRNN模型训练的土壤含水量和电导率的RMSE范围分别为7.01~14.70和7.60~13.70。利用生成的LSTM模型和气象数据进行拟合,将土壤含水量和电导率的预测值与实测值进行回归分析,LSTM模型拟合的土壤含水量和电导率的R²范围分别为0.760~0.906和0.648~0.850,GRNN模型拟合的土壤含水量和电导率的R²范围分别为0.126~0.369和0.132~0.268,说明LSTM模型的性能表现较好。结论 建立了柑橘果园环境的物联网信息传输系统,构建的基于LSTM的果园土壤含水量和电导率预测模型具有较高的精度,可用于指导柑橘果园的灌溉施肥管理。     

南亚热带典型人工林土壤饱和导水率特征及其影响因素研究

  目的  分析南亚热带典型人工林土壤饱和导水率的变化特征及其影响因素,了解水分在土壤中的运动规律,为进一步研究人工林土壤水分运动规律提供基础科学理论支持。  方法  以广西国有高峰林场内的马尾松、杉木、尾巨桉、米老排、红锥等人工林土壤为研究对象,采用恒定水头法测定5种人工林地的土壤饱和导水率,利用相关性分析和灰色关联分析法等数学分析方法,探讨南亚热带人工林土壤饱和导水率的变化特征及其影响因素。  结果  （1）杉木和马尾松2种人工林土壤的平均饱和导水率相对较高,尾巨桉人工林土壤的平均饱和导水率最低。不同人工林土壤饱和导水率随土壤深度均具有先减小后增加的变化规律,且土壤表层（0 ~ 10 cm）的饱和导水率均显著高于土壤下层（10 ~ 50 cm）。（2）相关性分析结果表明,有机质含量、大于0.2 mm的水稳性团聚体含量、总孔隙度、非毛管孔隙度、毛管孔隙度、饱和含水量和黏粒含量均与饱和导水率呈正相关关系,土壤密度与饱和导水率呈显著的负相关关系。（3）灰色关联分析结果表明,有机质含量、大于0.2 mm的水稳性团聚体含量、土壤密度、总孔隙度、饱和含水量、非毛管孔隙度和毛管孔隙度是土壤饱和导水率的重要影响因素,砂粒含量、粉粒含量和黏粒含量是次重要因素。  结论  相比红锥、尾巨桉和米老排,杉木和马尾松可以显著提高土壤饱和导水率,延缓地表径流产生,减少土壤侵蚀。应注重科学的人工林经营措施,以便有效提高土壤的导水与贮水能力。      

Using apparent soil electrical conductivity (EC<Subscript>a</Subscript>) to characterize vineyard soils of high clay content

The adoption of precision viticulture requires a detailed knowledge of variation in soil chemical, physical and profile properties. This study evaluates the usefulness of apparent electrical conductivity (EC_a) data within a GIS framework to identify variations in soil chemical and physical properties and moisture content. The work was conducted in a vineyard located in the Carneros Region (Napa Valley, California). The soil was sampled using 44 boreholes to quantify chemical and physical characteristics and 9 open pits to verify the borehole observations. Moisture content was determined using time domain reflectometry (TDR). To characterize soil EC_a, three campaigns were undertaken using a soil electrical conductivity meter (EM38). Linear regressions between soil EC_a and soil properties were determined. Boreholes and TDR data were interpolated by kriging to characterize the spatial distribution of soil variables. The resulting maps were compared to the results obtained using the best EC_a linear regressions. Using EC_a measurements, soil properties like extractable Na⁺ and Mg²⁺, clay and sand content were well estimated, while best estimates were obtained for extractable Na⁺ (r ²  = 0.770) and clay content (r ²  = 0.621). The best estimates for soil moisture content corresponded to moisture in the deeper soil horizons (r ²  = 0.449). The methods described above provided maps of soil properties estimated by EC_a in a GIS framework, and could save time and resources during vineyard establishment and management.     

Comparing apparent electrical conductivity measurements on a paddy field under flooded and drained conditions

Every growing season, paddy fields are kept both flooded and drained for a significant period of time. As a consequence, these soils develop distinct physico-chemical characteristics. For practical reasons, these soils are mostly sampled under dry conditions, but the question arises how representative the results are for the wet growing conditions. Therefore, the apparent electrical conductivity (EC_a) of a 1.4 ha alluvial paddy field located in the Brahmaputra floodplain of Bangladesh was measured in both dry and wet conditions by a sensing system using the electromagnetic induction sensor EM38, which does not require physical contact with the soil, and compared both surveys. Due to the smooth water surface under wet conditions which ensured increased stability of the sensing platform, the results of the survey showed considerably reduced micro-scale variability of EC_a. Furthermore, the wet survey results more reliably furnished soil-related information mainly due to the absence of soil moisture dynamics. The differences between EC_a under wet and dry conditions were attributed to differences in soil texture, mainly the sand content variation having considerable effect on soil moisture differences when flooded following drainage. Accordingly, the largest differences between EC_a under wet and dry conditions were found in those parts of the field with a large sand content. Hence, the conclusion was that an EC_a survey on flooded fields has an added value to precision soil management.     

Selection of ancillary data to derive production management units in sweet corn (<Emphasis Type="Italic">Zea Mays</Emphasis> var. rugosa) using MANOVA and an information criterion

In production systems where high-resolution harvest data are unavailable there is often a reliance on ancillary information to generate potential management units. In these situations correct identification of relevant sources of data is important to minimize cost to the grower. For three fields in a sweet corn production system in central NSW, Australia, several sets of high-resolution data were obtained using soil and crop canopy sensors. Management units were derived by k-means classification for 2–5 classes using three approaches: (1) with soil data, (2) with crop data and (3) a combination of both soil and crop data. Crop quantity and quality were sampled manually, and the sample data were related to the different management units using multivariate analysis of variance (MANOVA). The corrected Akaike information criterion (AICc) was then used to rank the different sources of data and the different orders of management units. For irrigated, short-season sweet corn production the management units derived from the crop canopy sensor data explained more variation in key harvest variables than management units derived from an apparent soil electrical conductivity (EC_a) survey or a mixture of crop and soil sensor data. Management units derived from crop data recorded just prior to side-dressing outperformed management units derived from data recorded earlier in the season. However, multi-temporal classification of early and mid-season crop data gave better results than single layer classification at any time. For all three fields in this study, a 3- or 4-unit classification gave the best results according to the information criterion (AICc). For growers interested in adopting differential management in irrigated sweet corn, investment in a crop canopy sensor will provide more useful high-resolution information than that in a high-resolution EC_a survey.