Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense datasets.     

Precision turfgrass management: challenges and field applications for mapping turfgrass soil and stress

Spatial and temporal variation of soil, climate, plants and irrigation requirements are challenges for modern agriculture and complex turfgrass sites. Precision agriculture (PA) evolved to improve site-specific management based on obtaining site-specific information. The focus of this concept paper is on the emerging area of precision turfgrass management (PTM) with attention given to: (a) comparing the concepts of PTM and PA in terms of driving forces and challenges that must be addressed for PTM to progress in science and practice and (b) discussion of specific field mapping applications (purposes) for different turfgrass situations such as golf courses, sod production fields and sports fields. The field applications relate to site-specific management of irrigation, salinity, fertilizer application and cultivation. To illustrate the potential for PTM, different approaches that may be necessary for PTM compared to PA are discussed. The initial factor that hindered the adoption of PTM has been the lack of mobile sensor platforms that can determine both key soil and plant properties for turfgrass situations. This paper concentrates on PTM field applications that involve mapping of both soil and plant attributes, in contrast to only optical sensing mapping.     

Precision Agriculture and Sustainability

Precision Agriculture (PA) can help in managing crop production inputs in an environmentally friendly way. By using site-specific knowledge, PA can target rates of fertilizer, seed and chemicals for soil and other conditions. PA substitutes information and knowledge for physical inputs. A literature review indicates PA can contribute in many ways to long-term sustainability of production agriculture, confirming the intuitive idea that PA should reduce environmental loading by applying fertilizers and pesticides only where they are needed, and when they are needed. Precision agriculture benefits to the environment come from more targeted use of inputs that reduce losses from excess applications and from reduction of losses due to nutrient imbalances, weed escapes, insect damage, etc. Other benefits include a reduction in pesticide resistance development. One limitation of the papers reviewed is that only a few actually measured directly environmental indices, such as leaching with the use of soil sensors. Most of them estimated indirectly the environmental benefits by measuring the reduced chemical loading. Results from an on-farm trial in Argentina provide an example of how site-specific information and variable rate application could be used in maintaining profitability while reducing N applications. Results of the sensitivity analysis show that PA is a modestly more profitable alternative than whole field management, for a wide range of restrictions on N application levels. These restrictions might be government regulations or the landowner's understanding of environmental stewardship. In the example, variable rate of N maintains farm profitability even when nitrogen is restricted to less than half of the recommended uniform rate.     

An on-farm approach to quantify yield variation and to derive decision rules for site-specific weed management

Grain yield often varies within agricultural fields as a result of the variation in soil characteristics, competition from weeds, management practices and their causal interactions. To implement appropriate management decisions, yield variability needs to be explained and quantified. A new experimental design was established and tested in a field experiment to detect yield variation in relation to the variation in soil quality, the heterogeneity of weed distribution and weed control within a field. Weed seedling distribution and density, apparent soil electrical conductivity (EC_a) and grain yield were recorded and mapped in a 3.5 ha winter wheat field during 2005 and 2006. A linear mixed model with an anisotropic spatial correlation structure was used to estimate the effect of soil characteristics, weed competition and herbicide treatment on crop yield. The results showed that all properties had a strong effect on grain yield. By adding herbicide costs and current grain price into the model, thresholds of weed density were derived for site-specific weed control. This experimental approach enables the variation of yield within agricultural fields to be explained, and an understanding of the effects on yield of the factors that affect it and their causal interactions to be gained. The approach can be applied to improve decision algorithms for the patch spraying of weeds.     

An integrated approach to site-specific management zone delineation

Dividing fields into a few relatively homogeneous management zones (MZs) is a practical and cost-effective approach to precision agriculture. There are three basic approaches to MZ delineation using soil and/or landscape properties, yield information, and both sources of information. The objective of this study is to propose an integrated approach to delineating site-specific MZ using relative elevation, organic matter, slope, electrical conductivity, yield spatial trend map, and yield temporal stability map (ROSE-YSTTS) and evaluate it against two other approaches using only soil and landscape information (ROSE) or clustering multiple year yield maps (CMYYM). The study was carried out on two no-till corn-soybean rotation fields in eastern Illinois, USA. Two years of nitrogen (N) rate experiments were conducted in Field B to evaluate the delineated MZs for site-specific N management. It was found that in general the ROSE approach was least effective in accounting for crop yield variability (8.0%–9.8%), while the CMYYM approach was least effective in accounting for soil and landscape (8.9%–38.1%), and soil nutrient and pH variability (9.4%–14.5%). The integrated ROSE-YSTTS approach was reasonably effective in accounting for the three sources of variability (38.6%–48.9%, 16.1%–17.3% and 13.2%–18.7% for soil and landscape, nutrient and pH, and yield variability, respectively), being either the best or second best approach. It was also found that the ROSE-YSTTS approach was effective in defining zones with high, medium and low economically optimum N rates. It is concluded that the integrated ROSE-YSTTS approach combining soil, landscape and yield spatial-temporal variability information can overcome the weaknesses of approaches using only soil, landscape or yield information, and is more robust for MZ delineation. It also has the potential for site-specific N management for improved economic returns. More studies are needed to further evaluate their appropriateness for precision N and crop management.     

Within and between-field spatial variation in soil phosphorus in permanent grassland

Soil phosphorus (P) concentrations above certain critical thresholds are a problem in many areas leading to its transport into surface and ground waters. Site-specific nutrient applications and the development of nutrient management plans for farms would help to optimize nutrient applications, meet crop requirements and take into consideration current soil nutrient status. In Northern Ireland, high concentrations of soil P are common, whereas low concentrations of soil potassium (K) and sulphur (S) have been reported in many silage fields. This study used grid and transect soil sampling to measure within- and between-field spatial variation in soil Olsen-P status across a 50-ha permanent grassland site used for silage production. Soil phosphorus indices ranged from Index 1 to Index 4 within single fields. The spatial patterns of soil P across fields suggested that there was scope for site-specific P fertilizer applications, with variable quantities of P being applied to different fields and within individual fields. Site-specific nutrient management has the potential to reduce excess P applications in some areas and avoid deficiencies in others, thereby minimizing environmental problems and optimizing yield.     

Site-specific early season potato yield forecast by neural network in Eastern Canada

Deterministic potato (Solanum tuberosum L.) growth models hardly rely on driving seasonal field variables that directly characterize spatial variation of plant growth. For example, the SUBSTOR model computes the leaf area index (LAI) as an auxiliary variable from meteorological conditions and soil properties. Empirical models may account for seasonal LAI functions and accurately predict potato yield. The objective was to evaluate multiple linear regression (MLR) and neural networks (NN) as predictive models of potato yield. Using data from several replicated on-farm experiments conducted over 3 years, model performance was evaluated for their capacity to forecast tuber yields 9, 10 and 11 weeks before harvest compared to SUBSTOR. A 3-input NN using LAI functions and cumulative rainfall yielded the most accurate estimations and forecasts of tuber yields. This NN showed that tuber yield of contrasting zones was mostly a function of meteorological conditions prevailing during the first 5–8 weeks after planting. Subsequent development of tubers was essentially controlled by biomass allocation to tubers. The NN models were more coherent than MLR and SUBSTOR for two reasons: (1) the use of seasonal LAI directly as input rather than computed as an auxiliary variable and (2) the non-linearity of the modeling process resulting in more accurate estimation of the temporal discontinuities of potato tuber growth. This model showed potential for application in precision agriculture by accounting for temporal and spatial real-time climatic and crop data.     

Site-specific production functions for variable rate corn nitrogen fertilization

Specific recommendations for variable rate nitrogen (VRN) fertilization in corn (Zea mays L.) are required to realize the potential environmental and economic benefits of this technology. However, recommendations based on algorithms that consider the processes controlling crop response to nitrogen fertilizer (NF) within fields have not yet been developed. The objectives of this study were to develop site-specific corn yield production functions for VRN fertilization and to determine the site-specific variables controlling corn response to NF. The experiments were conducted on eight commercial production fields. Fields were divided into 13–20 sections composed of five plots. Each plot received one NF rate. Site-specific variables included primary and secondary terrain attributes, and the Illinois Soil Nitrogen Test (ISNT). Nitrogen fertilizer significantly increased corn yield and it interacted with at least one site-specific variable. The ISNT was the site-specific variable that interacted with NF in most fields where the CV of ISNT was larger than 10%. The parameter estimates indicate that ISNT had a positive effect on corn yield and that it reduced the response to NF. Terrain attributes also affected corn yield and its response to NF. In general, parameter estimates indicated that well drained areas (i.e. small specific catchment area, moderate slopes) had higher yields and responded less to NF than areas where water is expected to accumulate. These results indicate that terrain attributes as surrogates for soil water content and the ISNT as a measure of soil mineralizable nitrogen are site-specific characteristics that affect corn yield and its response to NF.     

Delineating productivity zones in a citrus grove using citrus production,tree growth and temporally stable soil data

The productivity of a citrus grove with variation in tree growth was mapped to delineate zones of productivity based on several indicator properties. These properties were fruit yield, ultrasonically measured tree canopy volume, normalized difference vegetation index (NDVI), elevation and apparent electrical conductivity (EC_a). The spatial patterns of soil series, soil color and EC_a, and their correspondence with the variation in yield emphasized the importance of variation in the soil in differentiating the productivity of the grove. Citrus fruit yield was positively correlated with canopy volume, NDVI and EC_a, and yield was negatively correlated with elevation. Although all the properties were strongly correlated with yield and were able to explain the productivity of the grove, citrus tree canopy volume was most strongly correlated (r = 0.85) with yield, explaining 73% of its variation. Tree canopy volume was used to classify the citrus grove into five productivity zones termed as ‘very poor’, ‘poor’, ‘medium’, ‘good’ and ‘very good’ zones. The study showed that productivity of citrus groves can be mapped using various attributes that directly or indirectly affect citrus production. The productivity zones identified could be used successfully to plan soil sampling and characterize soil variation in new fields.     

A model for the spatial prediction of water status in vines (<Emphasis Type="Italic">Vitis vinifera</Emphasis> L.) using high resolution ancillary information

This paper establishes and tests a model to extrapolate vine water status spatially across a vineyard block. The proposed spatial model extrapolates predawn leaf water potential (PLWP), measured at a reference location, to other unsampled locations using a linear combination of spatial ancillary information sources (AIS) and the reference measurement. In the model, the reference value accounts for temporal variability and the AIS accounts for spatial variation of vine water status, which enables extrapolation over the whole domain (vine fields in this case) at any time when a reference measurement is made. The spatial model was validated for two fields planted with Syrah and Mourvèdre during the seasons 2003–2004 and 2005–2006, respectively, in the south of France. The proposed spatial model significantly improved the prediction of vine water status, especially under conditions of high water restriction (PLWP < −0.4 MPa), compared with a non-spatial model. The model was robust to the choice of reference site. The results also highlighted that AIS pertaining to canopy growth are the most relevant variables for predicting PLWP under these experimental conditions. Preliminary results showed the potential to calibrate the model from a limited number of field measurements, making it a realistic option for adoption in commercial vineyards. The success of the spatial model in improving the quality of prediction of PLWP means it could be incorporated into a decision-support tool to improve irrigation management within a vineyard.     

Use of soil nitrogen parameters and texture for spatially-variable nitrogen fertilization

Recent studies have demonstrated the potential importance of using soil texture to modify fertilizer N recommendations. The objective of this study was to determine (i) if surface clay content can be used as an auxiliary variable for estimating spatial variability of soil NO₃–N, and (ii) if this information is useful for variable rate N fertilization of non-irrigated corn [Zea mays (L.)] in south central Texas, USA across years. A 64 ha corn field with variable soil type and N fertility level was used for this study during 2004–2007. Plant and surface and sub-surface soil samples were collected at different grid points and analyzed for yield, soil N parameters and texture. A uniform rate (UR) of 120 kg N ha⁻¹ in 2004 and variable rates (VAR) of 0, 60, 120, and 180 kg N ha⁻¹ in 2005 through 2007 were applied to different sites in the field. Distinct yield variation was observed over this time period. Yield and soil surface clay content and soil N parameters were strongly spatially structured. Corn grain yield was positively related to residual NO₃–N with depth and either negatively or positively related to clay content depending on precipitation. Residual NO₃–N to 0.60 and 0.90 m depths was more related to corn yield than from shallower depths. The relationship of clay content with soil NO₃–N was weak and not temporally stable. Yield response to N rate also varied temporally. Supply of available N with depth, soil texture and growing season precipitation determined proper N management for this field.     

Spatio-temporal consideration of soil conditions and site-specific management of nematodes

Site-specific (precision) management (SSM) has potential for application in managing nematodes and soil conditions in environmentally meaningful ways. Successful application of SSM, however, may be dependent on how agronomically, biologically, and ecologically integrated the plan in question is. Otherwise, SSM risks falling into the “Tried but did not last” category. With this background and in addition to describing the concepts and principles of SSM, this presentation discusses the following interrelated points: (1) Case studies of spatio-temporal analysis of soybean cyst nematode (Heterodera glycines) infestations, soil conditions and crop yield in managed ecosystems. Among the critical factors to an accurate and sustained application of SSM are understanding (i) the temporal structure and (ii) the spatial structure of the attribute in question, and (iii) establishing cause-and-effect relationships in the prevailing conditions. New approaches to temporal structure analysis when balancing the purpose of SSM application and nematode biology (as it relates to life stages), population density in soil and root tissue (to determine threshold), and damage functions (physiological stress of the plant during the growing season) are outlined. (2) Application of the concept of fertiliser use efficiency (FUE) to identify soil conditions when managing soil fertility. Defined as increase in host productivity and/or decrease in plant-parasitic nematode population density in response to a given fertiliser treatment, the FUE model recognizes variable responses and identifies four categories of interactions necessary for integrated management decision-making options that account for agronomic, economic, ecological and environmental and pest management issues. (3) Approaches to changing soil conditions in agro-biologically integrated ways. By incorporating nematode community structure (an excellent indicator of soil bio-ecological changes), soil nutrient amendments and crop yield, we have described a modification of the FUE model to identify and monitor changes in soil conditions, thereby creating the necessary bridges to disciplinary and cross-disciplinary gaps and interactions.     

Can Landform Stratification Improve Our Understanding of Crop Yield Variability?

Farmers account for yield and soil variability to optimize their production under mainly economic considerations using the technology of precision farming. Therefore, understanding of the spatial variation of crop yield and crop yield development within arable fields is important for spatially variable management. Our aim was to classify landform units based on a digital elevation model, and to identify their impact on biomass development. Yield components were measured by harvesting spring barley (Hordeum vulgare, L.) in 1999, and winter rye (Secale cereale, L.) in 2000 and 2001, respectively, at 192 sampling points in a field in Saxony, Germany. The field was stratified into four landform units, i.e., shoulder, backslope, footslope and level. At each landform unit, a characteristic yield development could be observed. Spring barley grain yields were highest at the level positions with 6.7 t ha⁻¹ and approximately 0.15 t ha⁻¹ below that at shoulder and footslope positions in 1999. In 2000, winter rye harvest exhibited a reduction at backslope positions of around 0.2 t ha⁻¹ as compared to the highest yield obtained again at level positions with 11.1 t ha⁻¹. The distribution of winter rye grain yield across the different landforms was completely different in 2001 from that observed in 2000. Winter rye showed the highest yields at shoulder positions with 11.1 t ha⁻¹, followed by the level position with 0.5 t ha⁻¹ less grain yield. Different developments throughout the years were assumed to be due to soil water and meteorological conditions, as well as management history. Generally, crop yield differences of up to 0.7 t ha⁻¹ were found between landform elements with appropriate consideration of the respective seasonal weather conditions. Landform analysis proved to be helpful in explaining variation in grain yield within the field between different years.     

叶菜型甘薯栽培技术及施氮安全研究

为了探索叶菜型甘薯高产栽培安全施肥技术以及对其进行产业化开发,采用不同种植密度、不同肥料类型、不同施肥水平以及测定作菜食用部分硝酸盐积累情况等试验,初步明确了中长蔓分枝品种适宜种植密度为每公顷30~33万株,短蔓分枝强的品种适宜种植密度为每公顷27~30万株。施足肥料、多施氮肥对提高茎叶产量和茎叶鲜嫩度有明显效果,多施有机肥、拉长追肥间隔期以及优化配方施肥是降低茎叶硝酸盐含量行之有效的措施;而培育壮苗、合理密植、及时采摘、清洁园地、优化施肥、重施基肥以及加强管理、运用水肥促控技术、调节茎叶生长速度是叶菜型甘薯高产栽培与施氮安全关键技术措施。     

Underlying causes of yield spatial variability and potential for precision management in rice systems

Our current understanding of the mechanisms driving spatiotemporal yield variability in rice systems is insufficient for effective management at the sub-field scale. The overall objective of this study was to evaluate the potential of precision management for rice production. The spatiotemporal properties of multiyear yield monitor data from four rice fields, representing varying soil types and locations within the primary rice growing region in California, were quantified and characterized. The role of water management, land-leveling, and the spatial distribution of soil properties in driving yield heterogeneity was explored. Mean yield and coefficient of variation at the sampling points within each field ranged from 9.2 to 12.1 Mg ha^?1 and from 7.1 to 14.5 %, respectively. Using a k-means clustering and randomization method, temporally stable yield patterns were identified in three of the four fields. Redistribution of dissolved organic carbon, nitrogen, potassium and salts by lateral flood water movement was observed across all fields, but was only related to yield variability via exacerbating areas with high soil salinity. The effects of cold water temperature and land-leveling on yield variability were not observed. Soil electrical conductivity and/or plant available phosphorus were identified as the underlying causes of the within-field yield patterns using classification and regression trees. Our results demonstrate that while the high temporal yield variability in some rice fields does not permit precision management, in other fields exhibiting stable yield patterns with identifiable causes, precision management and modified water management may improve the profitability and resource-use efficiency of rice production systems.     

Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat

Several methods were developed for the redistribution of nitrogen (N) fertilizer within fields with winter wheat (Triticum aestivum L.) based on plant and soil sensors, and topographical information. The methods were based on data from nine field experiments in nine different fields for a 3-year period. Each field was divided into 80 or more subplots fertilized with 60, 120, 180 or 240 kg N ha⁻¹. The relationships between plot yield, N application rate, sensor measurements and the interaction between N application and sensor measurements were investigated. Based on the established relations, several sensor-based methods for within-field redistribution of N were developed. It was shown that plant sensors predicted yield at harvest better than soil sensors and topographical indices. The methods based on plant sensors showed that N fertilizer should be moved from areas with low and high sensor measurements to areas with medium values. The theoretical increase in yield and N uptake, and the reduced variation in grain protein content resulting from the application of the above methods were estimated. However, the estimated increases in crop yield, N-uptake and reduced variation in grain protein content were small.     

基于WebGIS和知识模型的精确农作决策支持系统

在提炼和优化作物管理知识模型的基础上,以WebGIS为空间信息平台,运用软构件技术及B/S分布式网络结构,构建了网络化精确农作决策支持系统.系统实现了基本地图操作、信息查询、差异分析、决策支持、结果展示以及系统维护功能,其中决策支持功能可基于田区土壤肥力和苗情长势差异进行数字化管理方案生成和因苗动态调控.在水稻试验示范区的系统应用结果表明,根据系统推荐的方案进行水稻田间管理,可使该区整体产量水平提高12.16%,肥料施用量减少23.55%,产量变异度下降79.49%.研究结果为实现网络化、数字化、广适性的精确农作决策支持提供了基本平台.     

Are precision agriculture tools and methods relevant at the whole-vineyard scale?

Precision viticulture (PV) has been mainly applied at the field level, for which the ability of high resolution data to match within-field variability has been already shown. However, the interest of PV for grape growers would be greater if its principles could also apply at a larger scale, as most growers still focus their management on a multi-field scale, not considering each field as an isolated unit. The aim of this study was to analyse whether it is possible and relevant to use PV tools to define meaningful management zones at the whole-vineyard scale. The study was carried out on a 90-ha vineyard made of 27 contiguous fields. The spatial variability of vine vigour, estimated with the Normalized Difference Vegetation Index (NDVI), was analysed at within-field and whole-vineyard scales. The spatial variability of the vigour was significant and spatially organized whatever the considered scale. Besides, vineyard spatial variability was characterised using information on environmental factors (soil apparent conductivity and elevation) and vine response (yield, vigour and grape composition). At both scales, NDVI and measured environmental factors were used to establish a three-level classification, whose agronomic significance was tested comparing the vine response observed for each class. The analysis of high resolution information allowed the definition of classes with agronomic and oenological implications, although there was not a straightforward correspondence between the classes defined and quality. Analysing the variability at the whole-vineyard scale highlighted a trend of spatial variation associated to elevation that was hardly visible at the within-field level.