scholarly journals The development and application of functions describing pasture yield responses to phosphorus, potassium and sulfur in Australia using meta-data analysis and derived soil-test calibration relationships

2019 ◽  
Vol 70 (12) ◽  
pp. 1065 ◽  
Author(s):  
Cameron J. P. Gourley ◽  
David M. Weaver ◽  
Richard J. Simpson ◽  
Sharon R. Aarons ◽  
Murray M. Hannah ◽  
...  
Keyword(s):  
Application Rate ◽  
Economic Benefits ◽  
National Database ◽  
Yield Response ◽  
Soil Test ◽  
Test Interpretation ◽  
Meta Data ◽  
Nutrient Use ◽  
Yield Responses ◽  
Pasture Yield

An improved ability to predict pasture dry matter (DM) yield response to applied phosphorus (P), potassium (K) and sulfur (S) is a crucial step in determining the production and economic benefits of fertiliser inputs and the environmental benefits associated with efficient nutrient use. The adoption and application of soil testing can make substantial improvements to nutrient use efficiency, but soil test interpretation needs to be based on the best available and most relevant experimental data. This paper reports on the development of improved national and regionally specific soil test–pasture yield response functions and critical soil test P, K and S values for near-maximum growth of improved pastures across Australia. A comprehensive dataset of pasture yield responses to fertiliser applications was collated from field experiments conducted in all improved pasture regions of Australia. The Better Fertiliser Decisions for Pastures (BFDP) database contains data from 3032 experiment sites, 21918 yield response measures and 5548 experiment site years. These data were converted to standard measurement units and compiled within a specifically designed relational database, where the data could be explored and interpreted. Key data included soil and site descriptions, pasture type, fertiliser type and rate, nutrient application rate, DM yield measures and soil test results (i.e. Olsen P, Colwell P, P buffering, Colwell K, Skene K, exchangeable K, CPC S, KCl S). These data were analysed, and quantitative non-linear mixed effects models based upon the Mitscherlich function were developed. Where appropriate, disparate datasets were integrated to derive the most appropriate response relationships for different soil texture and P buffering index classes, as well as interpretation at the regional, state, and national scale. Overall, the fitted models provided a good fit to the large body of data, using readily interpretable coefficients, but were at times limited by patchiness of meta-data and uneven representation of different soil types and regions. The models provided improved predictions of relative pasture yield response to soil nutrient status and can be scaled to absolute yield using a specified maximal yield by the user. Importantly, the response function exhibits diminishing returns, enabling marginal economic analysis and determination of optimum fertiliser application rate to a specific situation. These derived relationships form the basis of national standards for soil test interpretation and fertiliser recommendations for Australian pastures and grazing industries, and are incorporated within the major Australian fertiliser company decision support systems. However, the utility of the national database is limited without a contemporary web-based interface, like that developed for the Better Fertiliser Decisions for Cropping (BFDC) national database. An integrated approach between the BFDP and the BFDC would facilitate the interrogation of the database by advisors and farmers to generate yield response curves relevant to the region and/or pasture system of interest and provides the capacity to accommodate new data in the future.

Predicting pasture yield response to nitrogenous fertiliser in Australia using a meta-analysis-derived model, with field validation

Soil Research ◽  
2017 ◽  
Vol 55 (6) ◽  
pp. 567 ◽  
Author(s):  
Cameron J. P. Gourley ◽  
Murray C. Hannah ◽  
Kohleth T. H. Chia
Keyword(s):  
Fixed Effects ◽  
Meta Analysis ◽  
Large Body ◽  
Application Rate ◽  
Economic Benefits ◽  
Environmental Benefits ◽  
Yield Response ◽  
Independent Field ◽  
Maximal Yield ◽  
Pasture Yield

An improved ability to predict pasture dry matter (DM) yield response to applied nitrogen (N) is a crucial step in determining the production and economic benefits of N fertiliser inputs with associated environmental benefits from reducing inefficient N fertiliser use. Pasture DM yield responses to applied N fertiliser from 920 independent field trial sites were used from a database repository of Australian fertiliser experiments. These data were analysed and a quantitative non-linear mixed-effects model based on the Mitscherlich function was developed. The fitted model provided a good fit to a large body of data (R2 = 0.92), using readily interpretable coefficients, including fixed effects for state by season, phosphorus status and harvest type (initial or residual), and nested random effects for location and trial or subtrial. The model was limited by patchiness of metadata, uneven representation of regions and few very high rates of applied N in the data. Nonetheless, model predictions were comparable with independent spring pasture DM responses to applied N fertiliser from subsequent field studies on three contrasting pastures on commercial dairy farms in Victoria. The final derived model can be used to predict pasture yield response to applied N fertiliser as a proportion of obtainable yield and can be scaled to absolute response using the fitted model estimates of maximal yield or, more usefully, a specified maximal yield by the user. Importantly, the response function exhibits diminishing returns, enabling marginal economic analysis and determination of optimum N fertiliser application rate to a specified pasture.

Soil and tissue tests to predict pasture yield responses to applications of potassium fertiliser in high-rainfall areas of south-western Australia

2002 ◽  
Vol 42 (2) ◽  
pp. 149 ◽  
Author(s):  
M. D. A. Bolland ◽  
W. J. Cox ◽  
B. J. Codling
Keyword(s):  
Western Australia ◽  
Field Experiments ◽  
Relative Yield ◽  
Subterranean Clover ◽  
Test Method ◽  
Yield Response ◽  
Soil Test ◽  
Test Procedures ◽  
Pasture Production ◽  
Yield Responses

Dairy and beef pastures in the high (>800 mm annual average) rainfall areas of south-western Australia, based on subterranean clover (Trifolium subterraneum) and annual ryegrass (Lolium rigidum), grow on acidic to neutral deep (>40 cm) sands, up to 40 cm sand over loam or clay, or where loam or clay occur at the surface. Potassium deficiency is common, particularly for the sandy soils, requiring regular applications of fertiliser potassium for profitable pasture production. A large study was undertaken to assess 6 soil-test procedures, and tissue testing of dried herbage, as predictors of when fertiliser potassium was required for these pastures. The 100 field experiments, each conducted for 1 year, measured dried-herbage production separately for clover and ryegrass in response to applied fertiliser potassium (potassium chloride). Significant (P<0.05) increases in yield to applied potassium (yield response) were obtained in 42 experiments for clover and 6 experiments for ryegrass, indicating that grass roots were more able to access potassium from the soil than clover roots. When percentage of the maximum (relative) yield was related to soil-test potassium values for the top 10 cm of soil, the best relationships were obtained for the exchangeable (1 mol/L NH4Cl) and Colwell (0.5 mol/L NaHCO3-extracted) soil-test procedures for potassium. Both procedures accounted for about 42% of the variation for clover, 15% for ryegrass, and 32% for clover + grass. The Colwell procedure for the top 10 cm of soil is now the standard soil-test method for potassium used in Western Australia. No increases in clover yields to applied potassium were obtained for Colwell potassium at >100 mg/kg soil. There was always a clover-yield increase to applied potassium for Colwell potassium at <30 mg/kg soil. Corresponding potassium concentrations for ryegrass were >50 and <30 mg/kg soil. At potassium concentrations 30–100 mg/kg soil for clover and 30–50 mg/kg soil for ryegrass, the Colwell procedure did not reliably predict yield response, because from nil to large yield responses to applied potassium occurred. The Colwell procedure appears to extract the most labile potassium in the soil, including soluble potassium in soil solution and potassium balancing negative charge sites on soil constituents. In some soils, Colwell potassium was low indicating deficiency, yet plant roots may have accessed potassum deeper in the soil profile. Where the Colwell procedure does not reliably predict soil potassium status, tissue testing may help. The relationship between relative yield and tissue-test potassium varied markedly for different harvests in each year of the experiments, and for different experiments. For clover, the concentration of potassium in dried herbage that was related to 90% of the maximum, potassium non-limiting yield (critical potassium) was at the concentration of about 15 g/kg dried herbage for plants up to 8 weeks old, and at <10 g/kg dried herbage for plants older than 10–12 weeks. For ryegrass, there were insufficient data to provide reliable estimates of critical potassium.

Soil measurements and wheat yield response to superphosphate in Victoria

1975 ◽  
Vol 15 (72) ◽  
pp. 93
Author(s):  
B Palmer ◽  
VF McClelland ◽  
R Jardine
Keyword(s):  
Wheat Yield ◽  
Geographic Location ◽  
Yield Response ◽  
Soil Test ◽  
Soil Tests ◽  
Yield Data ◽  
Chemical Measurements ◽  
Soil Measurements ◽  
Yield Responses ◽  
Available Phosphate

The relationships between soil tests for 'plant available' phosphate and wheat yield response to applied superphosphate were examined and the extent to which these relationships were modified by other soil measurements was determined. Soil samples and wheat yield data were obtained from experiments conducted in the Victorian wheat belt. The sites were grouped into four relatively uniform classes using soil pH measurement and geographic location. The soil test values differed widely and were accountable for by the soil characteristics measured. However, the overall and within group yield responses to applied superphosphate could not be accounted for in terms of either the soil test value or the associated chemical measurements. By inference, yield response was clearly dependent on factors other than those determining the results of soil tests.

Soil nitrogen—crop response calibration relationships and criteria for winter cereal crops grown in Australia

2013 ◽  
Vol 64 (5) ◽  
pp. 442 ◽  
Author(s):  
Michael J. Bell ◽  
Wayne Strong ◽  
Denis Elliott ◽  
Charlie Walker
Keyword(s):  
Management Practices ◽  
Yield Potential ◽  
National Database ◽  
Soil Test ◽  
Mineral N ◽  
Winter Cereal ◽  
Available N ◽  
Nitrate N ◽  
The 1960S ◽  
Yield Responses

More than 1200 wheat and 120 barley experiments conducted in Australia to examine yield responses to applied nitrogen (N) fertiliser are contained in a national database of field crops nutrient research (BFDC National Database). The yield responses are accompanied by various pre-plant soil test data to quantify plant-available N and other indicators of soil fertility status or mineralisable N. A web application (BFDC Interrogator), developed to access the database, enables construction of calibrations between relative crop yield ((Y0/Ymax) × 100) and N soil test value. In this paper we report the critical soil test values for 90% RY (CV90) and the associated critical ranges (CR90, defined as the 70% confidence interval around that CV90) derived from analysis of various subsets of these winter cereal experiments. Experimental programs were conducted throughout Australia’s main grain-production regions in different eras, starting from the 1960s in Queensland through to Victoria during 2000s. Improved management practices adopted during the period were reflected in increasing potential yields with research era, increasing from an average Ymax of 2.2 t/ha in Queensland in the 1960s and 1970s, to 3.4 t/ha in South Australia (SA) in the 1980s, to 4.3 t/ha in New South Wales (NSW) in the 1990s, and 4.2 t/ha in Victoria in the 2000s. Various sampling depths (0.1–1.2 m) and methods of quantifying available N (nitrate-N or mineral-N) from pre-planting soil samples were used and provided useful guides to the need for supplementary N. The most regionally consistent relationships were established using nitrate-N (kg/ha) in the top 0.6 m of the soil profile, with regional and seasonal variation in CV90 largely accounted for through impacts on experimental Ymax. The CV90 for nitrate-N within the top 0.6 m of the soil profile for wheat crops increased from 36 to 110 kg nitrate-N/ha as Ymax increased over the range 1 to >5 t/ha. Apparent variation in CV90 with seasonal moisture availability was entirely consistent with impacts on experimental Ymax. Further analyses of wheat trials with available grain protein (~45% of all experiments) established that grain yield and not grain N content was the major driver of crop N demand and CV90. Subsets of data explored the impact of crop management practices such as crop rotation or fallow length on both pre-planting profile mineral-N and CV90. Analyses showed that while management practices influenced profile mineral-N at planting and the likelihood and size of yield response to applied N fertiliser, they had no significant impact on CV90. A level of risk is involved with the use of pre-plant testing to determine the need for supplementary N application in all Australian dryland systems. In southern and western regions, where crop performance is based almost entirely on in-crop rainfall, this risk is offset by the management opportunity to split N applications during crop growth in response to changing crop yield potential. In northern cropping systems, where stored soil moisture at sowing is indicative of minimum yield potential, erratic winter rainfall increases uncertainty about actual yield potential as well as reducing the opportunity for effective in-season applications.

scholarly journals Commercial Snap Bean Response to Fertilization with Broiler Litter

HortScience ◽  
1993 ◽  
Vol 28 (1) ◽  
pp. 29-31 ◽  
Author(s):  
James E. Brown ◽  
Charles H. Gilliam ◽  
Ronald L. Shumack ◽  
Daniel W. Porch
Keyword(s):  
Application Rate ◽  
Yield Response ◽  
Soil Test ◽  
Commercial Fertilizer ◽  
Broiler Litter ◽  
Snap Bean ◽  
Application Rates

Commercial snap bean (Phaseolus vulguris L.) yields in spring were similar when comparing a commercial fertilizer standard based on soil test recommendations to three application rates of broiler litter. Snap bean yields in the fall were higher on plots that received spring-applied broiler litter than on those receiving the commercial fertilizer standard in the fall. Increasing the application rate of broiler litter generally resulted in a linear yield response during both seasons.

Calibration and assessment of soil tests for estimating fertilizer requirements. I. Statistical models and tests of significance.

Soil Research ◽  
1967 ◽  
Vol 5 (2) ◽  
pp. 275 ◽  
Author(s):  
JD Colwell
Keyword(s):  
Field Experiments ◽  
Fertilizer Application ◽  
Application Rate ◽  
Yield Response ◽  
Soil Test ◽  
Regression Equations ◽  
Soil Tests ◽  
Yield Data ◽  
Fertilizer Application Rate ◽  
The Relationship

The calibration of soil tests requires a statistical model to describe the relationship between yield of crop, fertilizer application rate, and soil test. Yield response to fertilizers can be represented by polynomials both in the natural and square-root scales, and these polynomials can be generalized for a given crop and region, using soil test expressions. The generalization can be done using orthogonal polynomials and simultaneous regression equations that relate the coefficients of the polynomials to the soil test variables. This procedure is necessary because of heterogeneity in the residual sum of squares of regressions fitted to the yield data of several fertilizer field experiments within a region. The set of simultaneous regression equations constitutes a direct calibration of the soil test, since it can be used for the estimation of economic fertilizer requirement. Highly significant calibrations are demonstrated for a phosphorus soil test with wheat and a potassium test with potatoes. A nitrogen test gave only non-significant (P > 0.05) relationships.

Soil sulfur—crop response calibration relationships and criteria for field crops grown in Australia

2013 ◽  
Vol 64 (5) ◽  
pp. 523 ◽  
Author(s):  
Geoffrey C. Anderson ◽  
Ken I. Peverill ◽  
Ross F. Brennan
Keyword(s):  
Grain Yield ◽  
Web Application ◽  
Triticum Aestivum L ◽  
Soil Types ◽  
National Database ◽  
Yield Response ◽  
Soil Test ◽  
Sampling Depth ◽  
Brassica Napus L ◽  
R Value

Accurate definition of the sulfur (S) soil test–crop grain yield increase (response) relationship is required before soil S test measurements can be used to if there are likely to be responses to S fertilisers. An analysis was done using the Better Fertiliser Decision for Crops (BFDC) National Database using a web application (BFDC Interrogator) to develop calibration relationships between soil S tests (KCl-40 and MCP) using a selection of sampling depths and grain relative yields (RY). Critical soil test values (CSTV) and critical soil test ranges (CSTR) were defined at RY 90%. The ability of the KCl-40 extractable S soil test to predict grain yield response to applied S fertiliser was examined for wheat (Triticum aestivum L.) grown in Western Australia (WA), New South Wales (NSW), and Victoria and canola (Brassica napus L.) grown in WA and NSW. A smaller dataset using MCPi-extractable S was also assessed. The WA-grown wheat KCl-40 S CSTV, using sampling depth to 30 cm for soil types Chromosols (Coloured), Chromosols (Sesqui-Nodular), Kandosols (Grey and Yellow), Tenosols (Brown and Yellow), and Tenosols (Grey, Sesqui-Nodular), was 2.8 mg kg–1 with an associated CSTR 2.4–3.2 mg kg–1 and a correlation coefficient (r) 0.87. Similarly, KCl-40 S CSTV was defined using sampling depth to 10 cm for these selected soil types and for wheat grown on Vertosols in NSW. The accuracy of the KCl-40 S CSTV for canola grown in WA was improved using a sampling to a depth of 30 cm instead of 10 cm for all soil types. The canola KCl-40 S CSTV using sampling depth to 30 cm for these soil types was 7.2 mg kg–1 with an associated CSTR 6.8–7.5 and an r value 0.70. A similar KCl-40 S CSTV of 7.0 mg kg–1 was defined using a sampling depth of 10 cm, but the CSTR was higher (6.4–7.7 mg kg–1) and the r value lower (0.43). A lower KCl-40 S CSTV of 3.9 mg kg–1 or 31.0 kg ha–1 using a sampling depth of 60 cm was defined for canola grown in NSW using a limited number of S-rate calibration treatment series. Both MCPi (r = 0.32) and KCl-40 (r <0.20) soil S test–NSW canola response relationships using a 0–10 cm sampling depth were weak. The wheat KCl-40 S CSTR of 2.4–3.2 mg kg–1 can be used widely on soil types where soil sulfate is not leached during the growing season. However, both the WA canola CSTR of 6.4–7.2 mg kg–1 using a sampling depth of 30 cm and NSW canola CSTR of 25–39 kg ha–1 or 3.1–4.9 mg kg–1 using a sampling depth of 60 cm can be considered in regions outside of WA and NSW.

Determining the fertiliser phosphorus requirements of intensively grazed dairy pastures in south-western Australia with or without adequate nitrogen fertiliser

2007 ◽  
Vol 47 (7) ◽  
pp. 801 ◽  
Author(s):  
M. D. A. Bolland ◽  
I. F. Guthridge
Keyword(s):  
Western Australia ◽  
Field Experiments ◽  
Maximum Yield ◽  
Dairy Herd ◽  
Yield Response ◽  
Soil Test ◽  
Mineral N ◽  
P Leaching ◽  
The Many ◽  
Yield Responses

Fertiliser phosphorus (P) and, more recently, fertiliser nitrogen (N) are regularly applied to intensively grazed dairy pastures in south-western Australia. However, it is not known if applications of fertiliser N change pasture dry matter (DM) yield responses to applied fertiliser P. In three Western Australian field experiments (2000–04), six levels of P were applied to large plots with or without fertiliser N. The pastures were rotationally grazed. Grazing started when ryegrass plants had 2–3 leaves per tiller. Plots were grazed in common with the lactating dairy herd in the 6-h period between the morning and afternoon milking. A pasture DM yield response to applied N occurred for all harvests in all three experiments. For the two experiments on P deficient soil, pasture DM yield responses also occurred to applications of P. For some harvests when no fertiliser N was applied, probably because mineral N in soil was so small, there was a small, non-significant pasture DM response to applied P and the P × N interaction was highly significant (P < 0.001). However, for most harvests there was a significant pasture DM response to both applied N and P, and the P × N interaction was significant (P < 0.05–0.01), with the response to applied P, and maximum yield plateaus to applied P, being smaller when no N was applied. Despite this, for the significant pasture DM responses to applied P, the level of applied P required to produce 90% of the maximum pasture DM yield was mostly similar with or without applied N. Evidently for P deficient soils in the region, pasture DM responses to applied fertiliser P are smaller or may fail to occur unless fertiliser N is also applied. In a third experiment, where the soil had a high P status (i.e. more typical of most dairy farms in the region), there was only a pasture DM yield response to applied fertiliser N. We recommend that fertiliser P should not be applied to dairy pastures in the region until soil testing indicates likely deficiency, to avoid developing unproductive, unprofitable large surpluses of P in soil, and reduce the likelihood of P leaching and polluting water in the many drains and waterways in the region. For all three experiments, critical Colwell soil test P (a soil test value that was related to 90% of the maximum pasture DM yield), was similar for the two fertiliser N treatments.

Factors affecting yield and fertilizer response of sugar beet in Iran

1973 ◽  
Vol 81 (2) ◽  
pp. 311-316
Author(s):  
E. W. Bolle-Jones ◽  
F. Sanei
Keyword(s):  
Sugar Beet ◽  
Field Experiments ◽  
Phosphate Fertilizer ◽  
Fertilizer Application ◽  
Yield Response ◽  
Soil Test ◽  
Nitrogen And Phosphorus ◽  
Fertilizer Response ◽  
High Calcium ◽  
Yield Responses

SummaryField experiments were conducted in four provinces of Iran in which sugar-beet yield responses to added nitrogen and phosphorus fertilizers were correlated with soil test values and number of irrigations.Although significant yield responses to fertilizer application were obtained in all four provinces, extremely few significant relationships were established between soil test values and yield response.Average crop yield was favourably influenced by the number of irrigations applied in Fare and Khorasan, by organic carbon status in Esfahan and Khorasan and adversely affected by increased soil conductivity in Esfahan and Khorasan. These results were taken to imply an inadequate number of irrigations in Fars and Khorasan. The high calcium carbonate status found in Fars soil adversely affected the level of average yield.Response to nitrogen fertilizer declined in Fars and Khorasan as the leaf nitrogen exceeded 3·15 and 4·0% respectively. Response to phosphate fertilizer declined in West Azerbaijan and Khorasan when leaf phosphorus exceeded 0·4%.

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