James A. Taylor

A preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data

Abstract This paper proposes an Opportunity Index (Oi) for site-specific crop management (SSCM). In contrast to the traditional practice of uniform agronomic management, SSCM aims to match controllable inputs with spatially variable crop requirements. Farmers, however, are often left wondering how their yield maps can be used to justify a change to SSCM. The Oi is a single number that may be used in the process of this justification. The Oi is based on three components: (1) the magnitude of variation present in a yield map, relative to a certain threshold; (2) the average area within which yield is autocorrelated, relative to the minimum area within which variable-rate controllers (which physically implement SSCM) can reliably operate; and, (3) the economic and environmental benefit of SSCM relative to uniform management. The Oi was calculated for 20 Australian cropping fields and compared with b′ [Journal of Agricultural Sciences, Cambridge, 28 (1938)], which is an alternative method of quantifying the management opportunity from yield variation. A weak negative correlation was found to exist. Results suggest that a good opportunity for site-specific crop management exists when the Oi is greater than 20, although this is only a tentative recommendation.

Comparing the Ability of Multiple Soil Sensors to Predict Soil Properties in a Scottish Potato Production System

A soil survey with two soil sensors – an electromagnetic induction (EMI) sensor and a gamma radiometer – was conducted on a farm in south-east Scotland. The collected sensor data were used to direct soil sampling on the farm. The soil samples were then regressed against the sensor output to identify how well the sensor output predicted individual soil properties. The gamma radiometer produced better prediction fits in the topsoil than did the EMI sensor; however, the EMI predicted clay content better in the subsoil. Combining the sensor outputs produced improved fits for the topsoil data but not the subsoil. Neither sensor, individually or combined, produced good fits of soil pH. For potato production systems, topsoil properties are the dominant production determinants; thus a gamma radiometer using current configurations would be the preferred sensor in a single-sensor system assuming that all costs were equal. The economics of single vs. multiple sensor surveys is still unclear.

Site-specific variation in wheat grain protein concentration and wheat grain yield measured on an Australian farm using harvester-mounted on-the-go sensors.

Accurately measuring and understanding the fine-scale relationship between wheat grain yield (GY) and the concomitant grain protein concentration (GPC) should provide valuable information to improve the management of nitrogen inputs. Here, GPC and GY were monitored on-harvester for three seasons across 27 paddocks on an Australian farming enterprise using two independent, on-the-go sensing systems. A Zeltex Accuharvest measured GPC (%) and a John Deere GreenStar system measured GY (t/ha). Local calibration in each season for Australian spring wheat significantly improved the prediction accuracy, precision, and bias of the Zeltex Accuharvest when compared with the initial factory calibration. Substantial variation in GPC and GY was recorded at the field scale, with the least variation recorded in both parameters in the wetter season. GY (CV = 38%) was twice as variable on average as GPC (CV = 19%) across the enterprise. At this enterprise scale, a negative correlation between GPC and GY was observed for a composite of the field data from all seasons (r = –0.48); however, at the within-field scale the relationship was shown to vary from positive (max. = +0.41) to negative (min. = –0.65). Spatial variation in GPC and GY at the within-field scale was described best in the majority of cases by an exponential semivariogram model. Within-field spatial variability in GPC is more strongly autocorrelated than GY but on average they share a similar autocorrelated range (a′ = ~190 m). This spatial variability in GPC and GY gave rise to local spatial variation in the correlation between GPC and GY, with 85% of the fields registering regions of significant negative correlations (P < 0.01) and significant positive correlations observed in 70% of fields. The spatial pattern in these regions of significantly different correlations is shown to display spatial coherence from which inferences regarding the relative availability of soil nitrogen and moisture are suggested. The results point to the suitability of these on-the-go sensors for use in more sophisticated agronomic and environmentally targeted nitrogen-use analysis.

Causes of pattern in plant communities where environmental change is rapid and species longevity is short

Abstract Questions: To what extent can spatial structure and its causes be determined in a highly disturbed environment? What are the main determinants of pattern and are these species-specific? How much do spatial patterns change over generations? Location: Wimmera region of southern Australia. Methods: Broad-leaved weeds were counted in 225 000 contiguous 20-cm square quadrats. A substantial number of these quadrats were recorded again after two and four years. An hierarchical ‘adaptive analysis’ approach was used to select spatial analytical methods to examine specific aspects of pattern and variation in pattern from year to year. Results: Patterns varied among species and included both dense and sparse patches surrounded by areas of zero density, diffuse gradations of density and clear anisotropy. Patterns in Erodium botrys and Oxalis pes-caprae persisted over years, whereas patterns in Arctotheca calendula were less pronounced and varied over time. Edaphic factors appeared to have only a minor influence over the spatial distribution of the weed community as a whole. In Oxalis pes-caprae, whose patches were hypothesized to have been shaped by cultivation, there was no spread in four years, despite further tillage. Outlying plants of O. pes-caprae failed to establish new patches, even in the year of greatest population increase. Little evidence of localised recruitment events was found. Conclusions: Despite repeated annual disturbances by natural and anthropogenic mechanisms, clear and interpretable spatial structure develops in annual weeds over a range of spatial resolutions. Adaptive analysis is a useful approach to the characterization of such patterns. Abbreviations: ECe = Electrical conductivity; ECa = Apparent electrical conductivity; DTM = Digital Terrain Model. Nomenclature: Walsh & Entwisle (1994–1999).

Generalized analysis of spatial variation in yield monitor data

Australian lupin and cotton yield monitor data were analysed using spatial models from the Matern class of spatial covariance functions. Despite difficulties with the spatial disposition of the data, the analysis supports the statistical model in which the variation is a linear combination of white noise and the de Wijs process. The de Wijs process, also called the logarithmic covariance function, is a generalized covariance function that is conformally invariant and suggests that there is variation at all spatial scales. The present work also indicates that anisotropy and convolution are properties of yield monitor data and that it is hard to distinguish the two. The degree and causes of anisotropy require further investigation. Fitting this model is relatively easy for small, precision-agriculture datasets and open source software is available to this end. Comparing the de Wijs model with more general models in the Matern class is computationally intensive for precision-agriculture datasets.

A protocol for converting qualitative point soil pit survey data into continuous soil property maps

Vineyard soil surveys to date have focused on presenting soil data in point rather than raster format. This is due to the recording of both numeric and categorical variables. A protocol, including a lookup table to transform linguistic texture values into particle size distributions, to convert point data into continuous raster maps is presented. The resulting maps are coherent with vineyard knowledge and provide a strong spatial representation of soil variability within the vineyard. Validation with an independent dataset shows an error of ~10% in prediction; however, some of this can be attributed to errors in the geo-rectification of old data. Raster maps allow the survey data to be incorporated into computer systems to better model vineyard and irrigation designs and are more readily used in day-to-day vineyard management decisions.

Synthesis, resolution and rates of racemisation of 1-(2′-methyl-3′-indenyl)-2-naphthylamine and -2-naphthol

Abstract Axially chiral ligands 1-(2′-methyl-3′-indenyl)-2-naphthylamine 7 and -2-naphthol 8 have been prepared in three and four steps, respectively, from 2-nitro-1-naphthol. Following resolution by chiral HPLC, the absolute configurations were assigned by circular dichroism as (a R )-(−)- 7 and (a R )-(+)- 8 . The enantiomers of both ligands have significant thermal stability, with half-lives for racemisation of 73 h at 144°C and 220 h at 110°C, for 7 and 8 , respectively.

Analysis of the spatial variability of production attributes in sweet corn

Abstract A manual site-directed survey of yield, cob dimensions and plant densities is undertaken across three sweet corn (Zea mays var. rugosa) production fields in Bathurst, New South Wales, Australia, to assess if the spatial variation in production is sufficient to warrant further investment in site-specific studies. The 113 data points are assessed using non-spatial and spatial analysis and the data interpolated and mapped. Non-spatial analysis shows a large magnitude in all variables, particularly yield, which ranges from 6 to 30 Mg ha−1. Spatial analysis shows that all variables have moderate to strong spatial response and the resultant maps show strong spatial patterns that may be conducive to differential management strategies. The observed variation is similar to the variation reported in maize (Zea mays) where site-specific management has been successfully adopted.

Axially chiral cyclopentadienyl ligands: stereoselective synthesis of 1-substituted-9-(1′-naphthyl)fluorenes and retention of axial chirality in the fluorenyl carbanions1

1-(tert-Butyl- or 1-(p-tolyl-sulfinyl)naphthalene-2-carboxylate esters undergo coupling reactions with fluorenyllithiums substituted at the 1-position, providing 1-(1′-substituted-fluoren-9′-yl)naphthalene-2-carboxylate esters as single rotamers where the naphthalene ester substituent is syn to the fluorene 9-H. The stereoselectivity of the coupling reaction, with respect to asymmetric induction at the fluorene 9-C, varies from 21–95% (ee or de) dependant on the sulfoxide, ester and fluorene substituents, and the reaction temperature. The stereomutation of +ac(R)-1-methyl-9-(2′-methoxymethyl-1′-naphthyl)fluorene 33 into ent-33 was achieved through thermal atropisomerisation of 33 to the –sc(R)-rotamer 34, followed by lithiation of 34 and then reprotonation of the resultant fluorenyllithium 35, demonstrating the retention of axial chirality in the fluorenyl carbanion.

Early season detection and mapping of Pseudomonas syringae pv. actinidae infected kiwifruit (Actinidia sp.) orchards

Pseudomonas syringae pv. actinidiae (Psa) is an emerging disease of kiwifruit (Actinidia sp.). It has the potential to cause considerable production losses; therefore the ability to monitor and map the disease is important for industry-wide disease management. Using industry-collected infection data and an archived time-series of high-resolution satellite imagery, Psa disease monitoring in kiwifruit orchards was attempted for the 2010–11 growing season in the Bay of Plenty, New Zealand. Multiple vegetation indices were generated from imagery and a binomial logistic regression used to relate these vegetation indices to the Psa disease response. Results showed that the early season (2 October) photosynthetic vigour ratio was the most effective for differentiating infected and non-infected orchards. Omission and commission errors were observed, but were in part due to issues with data quality. The results were encouraging for the potential timely use of satellite imagery for monitoring and mapping Psa infections in kiwifruit.