Automatic harmonization of heterogeneous agronomic and environmental spatial data

Precision Agriculture - The analysis and mapping of agronomic and environmental spatial data require observations to be comparable. Heterogeneous spatial datasets are those for which the...     

Mapping subsoil acidity and shallow soil across a field with information from yield maps, geophysical sensing and the grower

Management decisions, such as subsoil liming or varying fertilizer inputs to take account of soil depth and anticipated yields require knowledge of where subsoil constraints to root growth occur across the field. We used selected yield maps based on criteria derived from crop simulation, apparent soil electrical conductivity (EC_a), gamma-ray emission maps and a soil type map drawn by the grower to predict the spatial distribution of subsoil acidity and shallow soil across a field. Yield maps integrate the effects of variation in soil and climate, and it was only under specific seasonal conditions that subsoil constraints depressed yields. We used crop simulation modelling to select yield maps with a large information content on the spatial distribution of these constraints and to omit those with potentially misleading information. Yield and other spatial data layers were used alone or in combination to develop subsoil mapping options to accommodate differences in data availability, access to precision agriculture techniques and the grower’s aptitude and preference. One option used gamma-ray spectrometry and EM38 survey as a dual-sensing system to improve data interpretation. Gamma-ray spectrometry helped to overcome the inability of current EC_a-based methods to sense soil depth in highly weathered sandy soil over cemented gravel. A feature of the approaches presented here is the use of grower and agronomist knowledge, and experience to help interpret the spatial data layers and to evaluate which approach is most suitable and likely to be adopted to suit an individual.     

A Comparison of Four Spatial Regression Models for Yield Monitor Data: A Case Study from Argentina

The gap between data analysis and site-specific recommendations has been identified as one of the key constraints on widespread adoption of precision agriculture technology. This disparity is in part due to the fact that analytical techniques available to understand crop GIS layers have lagged behind development of data gathering and storage technologies. Yield monitor, sensor and other spatially dense agronomic data is often autocorrelated, and this dependence among neighboring observations violates the assumptions of classical statistical analysis. Consequently, reliability of estimates may be compromised. Spatial regression analysis is one way to more fully exploit the information contained in spatially dense data. Spatial regression techniques can also adjust for bias and inefficiency caused by spatial autocorrelation. The objective of this paper is to compare four spatial regression methods that explicitly incorporate spatial correlation in the economic analysis of variable rate technology: (1) a regression approach adopted from the spatial econometric literature; (2) a polynomial trend regression approach; (3) a classical nearest neighbor analysis; and (4) a geostatistical approach. The data used in the analysis is from a variable rate nitrogen trial in the Córdoba Province, Argentina, 1999. The spatial regression approaches offered stronger statistical evidence of spatial heterogeneity of corn yield response to nitrogen than ordinary least squares. The spatial econometric analysis can be implemented on relatively small data sets that do not have enough observations for estimation of the semivariogram required by geostatistics. The nearest neighbor and polynomial trend analyses can be implemented with ordinary least squares routines that are available in GIS software. The main result of this study is that conclusions drawn from marginal analyses of this variable rate nitrogen trial were similar for each of the spatial regression models, although the assumptions about spatial process in each model are quite different.     

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.     

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.     

Investigating the Relationship Between Site-specific Yield and Protein of Cereal Crops

Agronomists use overlaying protein and yield maps to identify factors limiting cereal crop growth and development. Management decisions can be derived from knowing what and where these limiting factors are. In using protein and yield in this manner, there is an assumption that a physiologically or biologically significant relationship exists between grain protein and grain yield at the local level. In this paper, we investigate whether within-field yield and protein data support this relationship. The protein-yield relationship was modelled using weighted regression with global and local neighbourhoods in both 1-D and 2-D spatial location frameworks. The results from both the 1-D and 2-D analyses showed that the relationships between protein and yield are significant at both the macro (field level) (r²=0.25) and the micro-scale (local within field level) (r²=0.69). The assumption of a significant local relationship between protein and yield is supported by these data, suggesting that management decisions may be determined using such a relationship.     

Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (>?10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10?+?years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

     

小麦栽培管理知识模型系统的设计与实现

在总结、归纳和提炼小麦栽培理论与技术的研究成果和知识积累的基础上，运用系统学方法和结构化途径，建立了具有时空适应性的小麦栽培管理动态知识模型系统，用于确定不同环境条件和生产系统下的小麦生育特征及栽培管理技术方案，系统包括：产量水平与产量结构、主要品质指标、品种类型、播种期、基本苗、播种量、适宜生育期；主要生育期的茎蘖数、叶龄、叶面积指数、生物量等动态指标；有效叶面积率、高效叶面积率和粒叶比等源库指标；氮、磷、钾肥料运筹及主要生育期的水分管理等。本系统克服了传统作物栽培模式与专家系统的地域性强和广适性差的不足，从而为实现作物栽培管理的定量化和信息化奠定了基础。     

Defects in carbon nanostructures

Previous high-resolution electron microscopy (HREM) observations of the carbon nanotubes have led to a "Russian doll" structural model that is based on hollow concentric cylinders capped at both ends. The structures of the carbon nanotubes and particles were characterized here by bulk physical and chemical property measurements. The individual nanostructure is as compressible as graphite in the c axis, and such nanostructures can be intercalated with potassium and rubidium, leading to a saturation composition of "MC(8)." These results are counter to expectations that are based on a Russian doll structure. HREM after intercalation with potassium and deintercalation indicates that individual nanoparticles are a "paper-mache" of smaller graphite layers. Direct current magnetization and electron spin resonance measurements indicate that the electronic properties of the nanostructures are distinctly different from those of graphite. Although the nanostructures have distinct morphologies and electronic properties, they are highly defective and have a local structure similar to turbostratic graphite.     

Spatial analysis of yield monitor data: case studies of on-farm trials and farm management decision making

A 3-year case study was undertaken of how North American farmers use yield monitors for on-farm trials in farm management decision making. Case study methods were used because relatively few farmers quantitatively analyze yield monitor data. At this early research stage, insufficient farm management information about the data was available to ask the right questions in a large-scale survey. In addition to the formal case study of farmers experienced at using yield monitors to collect on-farm trial data, the study evaluated the effect of yield monitor data quality on farm decisions. Two levels of yield data quality included standard output where the default settings of farm-level mapping software were accepted and where filtering of the data was undertaken. Results indicated that yield data quality affects farm management decisions. In addition, farmers receiving a spatial analysis of their on-farm trial data tended to use split-field designs instead of replicated split-planter designs. They were also more confident in their decisions than before participation in the spatial analysis project, and made decisions more quickly.     

Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3–4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.     

Estimation of yield zones using aerial images and yield data from a few tracks of a combine harvester

Yield maps derived from yield mapping systems are often erroneous not only due to limitations in measuring the yield precisely but due to insufficient consideration of the requirements of yield mapping systems in practice as well. Aerial images of cultivated crop fields at an advanced growth stage frequently provide a spatial pattern similar to that of yield maps. Therefore, the possibility of generating a yield map using aerial images and measured yield data of a few tracks was examined for a period of 2 years in two fields grown with cereals. Yield zones based on Visible Atmospherically Resistant Index (VARI) values were compared with yield zones based on measured yield data of the whole field. About half of the grid cells of a field were allocated to the same yield zones irrespective of the mode of yield determination. Using the Kruskal–Wallis test, the data sub-sets of measured yield within the yield zones based on the VARI values differed significantly for all tested yield zones. As a result, the approach was successful in the case of these experimental sites.     

Use of Wavelets to Compare Simulated Yield Patterns for Precision Agriculture at the Field Scale

In this paper spatial patterns of simulated water limited potato production for a farm field in the Netherlands are analyzed using wavelets. The simulated yield patterns are decomposed using wavelets into crystals with a varying resolution. These crystals are used to relate the simulated spatial patterns to weather conditions. We compare simulated yield patterns from 10 successive years to arrive at a prototype patterns that can be used as a basis for site-specific management. Typical patterns for dry and wet years are quantified using wavelets. Wavelets provide a mechanism to distinguish between spatial patterns and allow a quantitative approach for classification of these spatial patterns.     

Enhancing the value of field experimentation through whole-of-block designs

Precision agriculture (PA) offers opportunities for the development of new approaches to on-farm experimentation to assist farmers with site-specific management decisions. Traditional agricultural experiments are usually implemented in fields with the least possible soil heterogeneity under the assumption that responses to inputs and inherent variation of the soil are additive components of yield variation. However, because the soil in typical fields is not homogeneous, PA has much to offer. Farmers faced with variable conditions need to optimize their management to the variation over space and time on their farm, a problem that is not solved by conventional approaches to experimentation. New designs for on-farm experiments were developed in the 1990s for cereal production in which the whole field was used for the experiment rather than small plots. We explore the extension of this type of experiment to a vineyard in the Clare Valley of South Australia aiming to evaluate options to increase grape yield and vine vigour. Manually sampled indices of vine performance measured on georeferenced ‘target’ grapevines were analysed geostatistically. The major advantage of such an approach is that the spatial variation in response to experimental treatments can be examined. Linear models of coregionalization, pseudo cross-variograms and standardized ordinary cokriging are used to map treatment responses over the experimental area and also the differences between them. The results indicate that both treatment responses and the significance of differences between them are spatially variable. Thus, we conclude that whole-of-block on-farm trials are useful in vineyards.     

Mapping management zones in a sandy pasture soil using an objective model and multivariate techniques

Soils occupied by dryland pastures usually have low fertility but can exhibit a high spatial variability. Consequently, logical application of fertilisers should be based on an appropriate knowledge of spatial variability of the main soil properties that can affect pasture yield and quality. Delineation of zones with similar soil fertility is necessary to implement site-specific management, reinforcing the interest of methods to identify these homogeneous zones. Thus, the formulation of the objective Rasch model constitutes a new approach in pasture fields. A case study was performed in a pasture field located in a montado (agrosilvopastoral) ecosystem. Measurements of some soil properties (texture, organic matter, nitrogen, phosphorus, potassium, cation exchange capacity and soil apparent electrical conductivity) at 24 sampling locations were integrated in the Rasch model. A classification of all sampling locations according to pasture soil fertility was established. Moreover, the influence of each soil property on the soil fertility was highlighted, with the clay content the most influential property in this sandy soil. Then, a clustering process was undertaken to delimit the homogeneous zones, considering soil pasture fertility, elevation and slope as the input layers. Three zones were delineated and vegetation indices (normalized difference vegetation index, NDVI, and normalized difference water index, NDWI) and pasture yield data at sampling locations were employed to check their differences. Results showed that vegetation indices were not suitable to detect the spatial variability between zones. However, differences in pasture yield and quality were evident, besides some key soil properties, such as clay content and organic matter.

     

The human as an experimental system in molecular genetics

There are compelling reasons for choosing to develop the human as the highest-order experimental system in genetics: an obvious social context that stirs interest, wide medical observation of the population that permits identification of an abundance of genetic defects, and our ability to perceive in the human subtle or complex variations that may not be observable in other species. Various lines of genetic inquiry that are based on research in other systems--cytogenetic analysis, biochemical studies, mapping of defective loci by linkage analysis in affected families, and in vitro techniques such as the creation of transgenic organisms--complement and enrich each other. New phenomena that would not have been predicted from investigations in other organisms have been found in humans, such as the discovery of the "giant" Duchenne muscular dystrophy gene and the identification of recessive cancer genes. Genetic research is yielding insights into human biology that are raising new possibilities for therapy and prevention of disease, as well as challenges to society in the form of ethical decisions about the appropriate application of genetic information.     

Is the crop insurance program effective in China? Evidence from farmers analysis in five provinces

This paper aims to evaluate the effectiveness of the Chinese crop insurance program in terms of farmers' utility and welfare. A simulation model based on the power utility function was first developed to evaluate the effectiveness of crop insurance. Then, the Monte Carlo approach was used to generate the datasets of area, price, yield, cost, and income based on the characteristics of representative farmers, which were clustered and calibrated using the farm-level data of 574 individual farmers from five Chinese provinces. Finally, the effectiveness of Chinese crop insurance was evaluated by comparing the certainty equivalence(CE) of farmers' utility/welfare under alternative crop insurance scenarios. Government subsidy is a necessary premise for implementing the crop insurance program. The government should subsidize more than 50% of the crop insurance premium to motivate more farmers to participate in the program. The findings also show that the current crop insurance program in China has increased the farmers' welfare but still need to be improved to achieve the Pareto improvement and to make full use of the financial fund of the government. This paper is believed to not only extend academic research but also has significant implications for policymakers, especially in the context of rapid development of Chinese crop insurance with much issues such as rate, subsidy and coverage level needed to be improved.