Relating minerals in rice shoots and grain to soil tests, yield and grain quality · Relating minerals in rice shoots and grain to soil tests, yield and grain quality Publication

Relating minerals in rice shoots

and grain to soil tests, yield and grain quality

A report for the Rural Industries Research and Development Corporation

by Graeme D Batten

August 2002

RIRDC Publication No 02/101 RIRDC Project No DAN-175A

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© 2002 Rural Industries Research and Development Corporation. All rights reserved. ISBN 0642 58500 8 ISSN 1440-6845 Relating minerals in rice shoots and grain to soil tests, yield and grain quality Publication No. 02/101 Project No. DAN-175A The views expressed and the conclusions reached in this publication are those of the author and not necessarily those of persons consulted. RIRDC shall not be responsible in any way whatsoever to any person who relies in whole or in part on the contents of this report. This publication is copyright. However, RIRDC encourages wide dissemination of its research, providing the Corporation is clearly acknowledged. For any other enquiries concerning reproduction, contact the Publications Manager on phone 02 6272 3186. Researcher Contact Details Graeme D. Batten Faculty of Science and Agriculture Charles Sturt University PO Box 588 Wagga Wagga NSW 2678 Phone 02 6933 4207 Fax 02 6933 2812 Email [email protected] In submitting this report, the researcher has agreed to RIRDC publishing this material in its edited form. RIRDC Contact Details Rural Industries Research and Development Corporation Level 1, AMA House 42 Macquarie Street BARTON ACT 2600 PO Box 4776 KINGSTON ACT 2604 Phone: 02 6272 4539 Fax: 02 6272 5877 Email: [email protected]. Website: http://www.rirdc.gov.au Published in August 2002 Printed on environmentally friendly paper by Canprint

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Foreword The high yields of rice in Australia are responsible for the removal of substantial amounts of nitrogen and minerals from the soil. The rate of mining of these nutrients from the soil will increase with yield increases. Further increases in yield are expected with the release of new varieties and improved management practices. The concentration and amount of mineral nutrients in the rice plant need to be monitored to: - allow losses from the soil to be calculated - check the influence of yield on mineral concentrations in rice plants - detect deficiencies that may affect yield and seedling vigour - indicate the food/feed value of the rice, and - assess the effects of crop management practices. This project investigates the relationship between the mineral concentrations in soil and in the rice plant shoots and grain. The variation in mineral concentrations in plant tissue within a rice paddock is also examined. The use of critical nutrient levels in rice shoots to provide a basis for diagnosing problem crops is also discussed. This project was funded from industry revenue which is matched by funds provided by the Federal Government. This report, a new addition to RIRDC’s diverse range of over 800 research publications, forms part of our Rice R&D program, which aims to provide nutrient management systems for profitable and sustainable rice production. Most of our publications are available for viewing, downloading or purchasing online through our website: • downloads at www.rirdc.gov.au/reports/Index.htm • purchases at www.rirdc.gov.au/pub/cat/contents.html Simon Hearn Managing Director Rural Industries Research and Development Corporation

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Acknowledgments This project supported Ms Kate Marr, Technical Officer who carried out field and laboratory studies with dedication and competence. We thank Brian Dunn from NSW Agriculture, Yanco Agricultural Institute and Craig Russell of the CRC for Sustainable Rice Production for providing soil and plant samples, the Appraisals Centre Staff from Ricegrowers’ Cooperative Limited Appraisals Laboratory for providing rice grain samples; Teresa Fowles and Lyndon Palmer from the Waite Institute, Adelaide for providing ICP analyses of plants and grains, Pivot Limited for analysing soil samples, and Sarah Spackman for preparing GIS maps.

Abbreviations Al - Aluminium Ca –Calcium CEC – Cation Exchange Capacity CIA – Coleambally Irrigation Area Cu – Copper EC – Electrical Conductivity Fe - Iron GIS – Geographic Information System ICP – Inductively Coupled Plasma emission spectroscopy K - Potassium Mg – Magnesium MIA –Murrumbidgee Irrigation Area Mn – Manganese Na - Sodium NDVI – Normalised Difference Vegetation Index NIR – Near Infrared P - Phosphorus PI – Panicle Initiation RVA – Rapid Visco Analyser S - Sulphur SAVI – Soil Adjusted Vegetation Index SR – Simple Ratio Zn - Zinc

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Contents Foreword ...........................................................................................................................iii Acknowledgments ............................................................................................................iv Abbreviations....................................................................................................................iv Contents .............................................................................................................................v Executive Summary..........................................................................................................vi 1. Introduction .............................................................................................................1

1.1 Mining of minerals from rice soils....................................................................................1 1.2 Minerals and rice grain quality .........................................................................................1 1.3 Mapping the variation in mineral concentrations within a paddock.................................2 1.4 Critical nutrient concentrations in whole rice shoots........................................................2

2. Objectives................................................................................................................3 3. Methodologies.........................................................................................................3

3.1 Sample collection..............................................................................................................3 3.1.1 Grain mineral / grain quality studies............................................................................... 3 3.1.2 Variation in grain minerals within a paddock................................................................. 3 3.1.3 Influence of soil factors on plant mineral concentrations, and critical nutrient

concentrations in whole rice shoots ................................................................................ 4 3.2 Chemical analyses.............................................................................................................4

4. Results .....................................................................................................................5

4.1 Influence of soil factors on shoot and grain mineral concentrations, and grain quality ...5 4.2 Grain mineral / grain quality study ..................................................................................11 4.3 Variation in shoot and grain mineral concentrations within and between rice paddocks ................................................................................................................................12

4.3.1 Ability to detect mineral variation by remotely sensed images .................................... 18 4.4 Critical nutrient concentrations in whole rice shoots to provide a basis for diagnosing

problem crops..................................................................................................................19 4.5 A Nutrient balance for an “average” Australian rice crop..............................................19

5. Implications ...........................................................................................................21 6. Recommendations ................................................................................................21 7. References.............................................................................................................22 8. Publications List ...................................................................................................24

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Executive Summary High rice yields in Australia (9.7 t/ha in 2001, 8.2 t/ha in 2000) are responsible for the rapid removal of nitrogen and plant essential nutrients from the soil. These high yielding crops require access to large amounts of nitrogen (and other elements) from the soil and from fertilisers. In the previous RIRDC project DAN 123A, we reported the concentrations and total amounts of mineral nutrients that are removed in the grain and shoots of rice plants. For example, in a typical 10 t/ha rice crop (cultivar Amaroo), about 100 kg N, 25 kg P, 9 kg S, 2 kg Ca, 1 kg Mn, and 160 g Zn are removed from the soil in the paddy (grain and hulls). Mineral fertilisation is mainly restricted to N applications. In the 2000-2001 season, rice farmers using the Rice Tissue Testing Service applied an average of 73 kg N per hectare. Less than half of the 1979 paddocks that were tested in the 2000/01 PI Tissue Tests had P applied to the soil. Only 14% of all paddocks received enough P fertiliser to provide an average 10 t/ha crop with its requirement for P (i.e. 25 kg P/ha). About 20% of all paddocks tested had adequate S applied (i.e. 8 kg S/ha). Zinc applications are usually confined to soils of high pH and newly exposed subsoils; about 5% of all paddocks were fertilized with Zn. The current project examined:

1. the relationships between soil mineral concentrations and the concentration of minerals taken up into the plant tissues and grain;

2. the influence of minerals, eg the Mg/K ratio, on the cooking quality of rice; 3. the variability of plant and grain mineral concentrations within a single paddock; 4. the ability of this variation to be detected by remotely sensed images is also explored; 5. the mineral concentrations in rice shoots and grain as a basis for diagnosing crops with

possible mineral deficiencies; and 6. the balance between the input and removal of nutrients in a rice cropping system.

Findings Soil P and pH levels are declining and soil sodium increasing. These factors will impact on rice yields. The current project showed that these soil properties reduce the concentrations of some plant-essential elements such as P, K and Mn and increase the concentrations of Na in shoot tissues and grains. These changes may also have an influence on grain quality parameters including average grain weight, %sterility, number of grains per panicle and cooking quality. Although we have found reasonably strong relationships between the Mg/K ration of brown Amaroo grain and cooking quality in 2 of 3 seasons, there is not enough evidence to date to conclude that the Mg/K ratio of brown grain significantly affects the cooking quality of the white rice grain. The variability of minerals across a single rice paddock is reported. The variation (CV) for macro-nutrients in grains was 3 to 10%, for nitrogen and some micro-nutrients 7 to 20%, and for sodium as high as 29%. Higher variability was reported for minerals in shoot samples collected at the panicle initiation stage. Remotely sensed images of several rice paddocks suggests that mapping of variability of macronutrients in shoots may be possible using ratios of visible and NIR wavelengths. Concentrations of nutrients which are associated with good rice yields are reported and these are a step towards defining the critical concentrations of elements.

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This study reports that the average Australian Rice crop removes more N, P, K and trace elements than are applied in fertilizers and irrigation water. Nitrogen is well managed, but P and Zn shortages are beginning to limit yields of crops on some farms. Monitoring crops for K shortages is advised, especially where stubbles are burned. This study has led to a better understanding of the nutrients of rice plants and the implications of changes in soil nutrients, soil acidification and salinity on rice production and clarified the need for soil amelioration to ensure sustainable yields and quality. The data from this project quantify the rates of input and removal of nutrients from soils during a rice crop season. Farmers and advisers in the rice industry have advised that the findings confirm anecdotal evidence of the importance of minerals to rice productivity. The long term impact of nutrient removal by rice will vary with the intensity of rice in the cropping sequence. More regular monitoring of soils for pH, available P, salinity and other nutrients is recommended as a basis for soil management and sustainable yields.

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1. Introduction 1.1 Mining of minerals from rice soils Australian rice growers achieve some of the highest yields in the world and are the most productive and intensive grain producers in Australia. This high level of production results in a high rate of removal (mining) of mineral elements from the soil. Long term rice cropping may lead to depletion of minerals from the soil, land degradation and yield decline. Knowledge of the relationships between fertiliser application and the amounts of minerals mined from the soil is required for the sustainable management of rice cropping systems. There is some information on nitrogen and mineral levels in Australian crops sampled at panicle initiation and at maturity. Rice producers using the NIR Rice Tissue Test in the 1994 season (Batten et al. 1994) added an average 124 kg N/ha to each crop (Blakeney et al., 1994). These rates of nitrogen fertiliser, even when applied prior to sowing to promote vegetative growth and raise yields, consistently raise grain nitrogen concentrations (Boerema 1974). In a study of the medium-grain genotype Amaroo, Marr et al. (1995) found that, from each hectare of soil, about 100 kg N, 25 kg P and 9 kg S was removed in the grain of an average 10 t/ha crop (14% moisture). In another study on the effect of nitrogen fertiliser on yield and mineral elements in rice (Marr et al. 1999), it was found that yield increase, driven by N fertiliser application, was the major influence on increased mining of N, S, P, K, Mg, Ca, Fe, Mn and Zn from the soil. The current project examines the relationship between soil mineral concentrations and the concentration of minerals taken up into the plant tissues and grain. 1.2 Minerals and rice grain quality The relationship between the Mg/K ratio in brown grain and the cooking quality of the white rice is considered to be important by Japanese workers (Okamoto et al. 1992, Itani et al. 1998). Okamoto et al. (1992) reported correlation coefficients of 0.46-0.49 in two years’ experiments, between the Mg/K ratio and the ‘stickiness’ of cooked rice, as measured by a sensory evaluation panel. A link between grain cooking quality and the mineral elements in Australian brown grain has been noted in the previous RIRDC Project DAN 123 - Defining the mineral levels in rice needed to maximise yield and quality. In the current project we compared the protein and Mg/K ratio of Australian rice grain from farmers paddocks, from 3 different seasons, with the cooking quality of the grain as measured by the rapid visco analyser (RVA). The relationship between soil minerals and grain quality has received little attention. In a pot experiment, Huang (1990) found a significant positive correlation between soil Ca and grain protein and gel consistency; and a significant negative correlation between soil Mg and grain protein. Other findings by Huang (1990) include a significant positive correlation between soil S and %wholegrain and grain protein; and a significant positive correlation between soil Mn and %chalk in the grain. The current project examines the relationships between the levels of some soil minerals and rice grain quality characteristics including harvest index, protein (from %N), average grain weight, %sterility and number of grains per panicle.

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1.3 Mapping the variation in mineral concentrations within a paddock Previous studies have reported the mineral concentrations in Australian brown rice grain and shoot material (Marr et al. 1995, 1999). The values reported are single representative samples from commercial farmers’ paddocks, or from experimental plots. These studies show a wide range in rice plant mineral concentrations between farms. This may be due to differences in soil type, paddock history or management practices. However there is a lack of information regarding the variation in rice mineral concentrations within a single paddock. It may be useful to examine the extent of this variability to locate deficient areas of the crop. For example, the deficient areas may be correlated to the distribution of soil characteristics (eg. P concentration). Fertiliser applications may then be tailored to the required areas, to improve yield and possibly grain quality. Geographic information systems (GIS) and remote sensing have been used to locate and measure the total area of rice grown in areas of southern Australia (Barrs and Prathapar 1994). Smith et al. (1987) used a GIS and image analysis system to extract relationships between soil factors, elevation, plant nutrient status and plant production in a single pasture paddock. In the present study, remotely sensed images of individual rice paddocks were taken at specific rice growth stages, including mid-tillering and panicle initiation (PI). The images were classified to produce maps showing different areas of biomass and soil mineral concentration. Maps showing the variability of eg. P in different areas of the paddock were overlayed with eg. soil type maps. These map overlays were used to assess whether there were any patterns linking the mineral levels in plants and the soil. 1.4 Critical nutrient concentrations in whole rice shoots Critical nutrient concentrations can be used to assess whether a crop may be deficient in certain essential minerals, which may lead to decreased yields. The critical nutrient concentration of a plant can be defined as the minimum nutrient concentration required for maximum growth rate at a given time. In this study we present mineral nutrient levels in rice shoots and grain, which enable comparisons with the amount of minerals present in the soil and also a basis to determine the balance between nutrient removals and nutrient inputs.

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2. Objectives To determine from soil, shoot and grain samples collected from rice farms with different soil types and rice histories and by intensive sampling of selected rice farms:

(i) the influence of soil factors on plant growth, nutrient uptake, grain yield and grain quality;

(ii) the variation in plant and grain minerals within a paddock and the ability to detect this variation by remote images; and

(iii) the critical nutrient concentrations in whole rice shoots to provide a basis for diagnosing problem crops.

3. Methodologies 3.1 Sample collection 3.1.1 Grain mineral / grain quality studies Paddy rice samples (approximately 200 grams) from commercial rice crops of the major rice varieties were obtained from the Appraisals Centre of Ricegrowers’ Cooperative Limited, Leeton. Samples from three seasons (1992-93, 1996-97 and 1998-99) were collected. These were used for analysing the mineral concentration and grain quality using the rapid visco analyser (RVA). Samples collected for studying the variation in grain minerals within a paddock (see 3.1.2) were also used for comparing soil mineral content and grain quality measurements including harvest index, average grain weight, percent sterility and average grain number per panicle. 3.1.2 Variation in grain minerals within a paddock Whole plant cuts and soil samples were taken from 50 points within four selected commercial rice paddocks in the MIA, NSW. The latitude and longitude of each sampling position was taken using a differential global positioning system. Plant samples were taken at the mid-tillering, PI, flowering and maturity growth stages, corresponding to the dates of 8 December 1998, 5 January, 3 March and 30 March 1999. Half-metre square plant samples were taken at each growth stage. The samples were dried to a constant mass in a dehydrator at about 600C, weighed and sub-samples were ground using a cyclone mill (Cereal Mill 6200, Newport Scientific, Australia). In addition, a sample of 30 tillers was collected at the maturity stage for grain quality analyses. These samples were dried in an air oven at 500C for two hours, weighed, and the percent of sterile grains calculated. The full mature grains were separated from the straw, dehulled and the brown grain ground. The straw was also ground separately. The ground shoot, grain, straw and soil samples were analysed for mineral concentration. At the same time as plant sampling, remotely sensed imagery was taken of the entire rice paddock. Images of the paddocks were acquired using a 4-camera airborne video system (Louis et al. 1995), at an altitude of 1440 m above ground level (Spackman et al. 2000). On-ground canvas calibration panels were included in each flight with coincident on-ground spectral reflectance measurements taken of the panels using a calibrated PSII field radiometer (Analytical Spectral Devices, Boulder, Colorado, USA).

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Each image was pre-processed for shear correction, band to band registration, and to eliminate geometric and radiometric distortion (Spackman et al. 2000). Image digital numbers (DNs) were converted to reflectance using the ground calibration panels. Reflectance values were converted into simple ratio (SR), normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI) images and correlated with ground biomass. Additionally, maps were created from the ground sampled soil and grain mineral information using minimum curvature surfaces. These maps were visually related to the remotely sensed images. 3.1.3 Influence of soil factors on plant mineral concentrations, and critical nutrient

concentrations in whole rice shoots In cooperation with Brian Dunn and Craig Russell (Rice CRC project 2.1.01), grain and shoot samples at PI from over 100 farms throughout the MIA, CIA and Murray Valley, were collected and analysed for mineral content. Samples were taken from 6 m x 6 m plots that had zero or commercial N rate fertiliser treatments. Soil samples were also taken from the farmers’ plots before the crop was sown. The soil mineral concentrations were compared to the total amount of minerals removed in the grain and plant tissue. 3.2 Chemical analyses Approximately 10 grams of paddy rice was dehulled, and the brown grain was ground in a ring mill (Rocklabs Ltd., Auckland NZ). Analysis of P, S, Ca, Mg, K, Na, Cu, Fe, Mn and Zn was carried out using the nitric acid digestion procedure recommended by Zarcinas et al. (1987). One gram of ground sample was digested with 10 ml 70%(v/v) HNO3 in a 200 ml tube capped with a glass bubble to reduce evaporation. After a 45 min pre-digestion at 900C, the temperature was increased to 1400C for a 1.5 hour digestion. The digests were diluted with distilled water and made up to 100 ml when cool. Aliquots were stored in 50 ml plastic vials ready for ICP analysis. Elements were determined in each digest using an ICP spectrophotometer (ARL 3580B, simultaneous/sequential). The moisture content of the samples was calculated using the AACC (1983) method 44-15A. Standard reference materials from the Australian Soil and Plant Analysis Council were analysed with each batch. Total nitrogen was analysed using a Dumas combustion method (LECO®; Elding 1968). Soil samples were analysed for “available” P, electrical conductivity (EC), pH and exchangeable cations at the commercial soil testing laboratories of Pivot Limited, Victoria.

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4. Results 4.1 Influence of soil factors on shoot and grain mineral

concentrations, and grain quality Table 1 presents a correlation matrix for relations between soil tests data and shoot mineral concentrations at PI for samples from 77 commercial crops. Soil available P was positively correlated with shoot P, K and Mn and negatively correlated with plant Na. Increases in soil exchangeable Na were associated with increases in shoot Na, and Mg and decreases in K, Ca and Zn. Higher CEC related positively to shoot Mg and Na but negatively to P, Zn and Fe Table 2 presents a summary the significant correlations between soil tests and grain minerals for the 77 commercial sites reported in Table 1. Grain P, K and Zn were negatively correlated with soil exchangeable K, Mg and Na. Table 3 is a correlation matrix that shows the relations between soil tests and the mineral concentrations in shoots at PI and brown rice grain, plus harvest index (HI), average grain weight, %sterility and average number of grains per panicle. The correlation matrix represents data collected from the four farmers’ paddocks (from MIA) used in the mineral variation study. The following conclusions are drawn from these data for soil – plant PI nutrient relations: - Soil P was positively correlated with PI shoot concentrations of N, P, Mg, Cu and Na but

negatively correlated with Fe, Zn and Al. - Soil EC was positively correlated with N, S, Cu, Ca, Mg, and Na, and negatively correlated

with K and Fe. - Soil pH was positively correlated with N, Mg, Cu and Na and negatively correlated with Fe,

Zn, and K. - CEC was positively correlated with Cu, Na, and P but negatively correlated with Zn, and K. The following conclusions are drawn from these data for soil – grain mineral relations: - Soil P was positively correlated with grain P and Mg but negatively correlated with grain Mn

and Ca. - Soil EC was negatively correlated with grain Mn. - Soil pH was negatively correlated with grain N, Mn and Ca. Soil pH and exchangeable Al were

highly correlated. - Soil CEC was negatively correlated with grain Mn, Ca and Mg. - Across these four paddocks, grain N was correlated with PI N (r = 0.26), PI Zn (r = 0.37), PI K

(r = 0.38), PI S (r= 0.38), %sterility (r = 0.70), grain %S (r = 0.91), Mn (r = 0.39), and grain Mg (r = -0.35).

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- Grain P concentration was positively correlated with soil P, PI P, PI Mn, grain Mg and grain K and negatively correlated with PI N, PI S PI Cu and PI Zn, %sterility and grain N.

The relations between soil and plant and grain properties are not consistent in the above data but there is some evidence to support the view that soil P influences plant growth through enhanced uptake of other essential nutrients and in some cases by reducing the uptake of Na. Soil Na or EC is linked to reduced concentrations of some macro- and micro-nutrients. The associations between soil, plant and grain minerals, plant yields and grain quality require closer scrutiny. To determine concentrations of minerals in rice grain which could be used in subsequent calculations the samples from commercial crops were used to determine average nutrient concentration values. The concentrations in brown rice are presented in Table 4a and the concentrations in paddy in Table 4b.

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Table 1. Significant linear correlations between soil and plant nutrient concentrations at PI for 77 commercial rice crops.

Fe Mn B Zn Ca Mg Na K P S Al Soil P Soil K Soil Ca Soil Mg

Soil Na

Tot CEC pH1 EC pH2

Fe 1.00

Mn 1.00

B 1.00

Zn 0.22 1.00

Ca 1.00

Mg 0.28 1.00

Na -0.21 -0.34 0.36 1.00

K -0.40 1.00

P 0.21 -0.24 0.47 1.00

S 0.39 0.29 0.49 1.00

Al 0.70 1.00

Soil P 0.22 -0.28 0.25 0.47 1.00

Soil K -0.23 0.26 0.27 1.00

Soil Ca -0.23 -0.29 0.31 0.24 -0.24 0.49 1.00

Soil Mg -0.23 -0.43 -0.25 0.49 0.53 -0.32 -0.25 0.35 0.82 1.00

Soil Na -0.30 -0.30 0.53 0.74 -0.38 -0.28 0.43 0.77 1.00

Tot CEC -0.21 -0.37 0.42 0.43 -0.26 0.40 0.88 0.94 0.67 1.00

pH1 1.00

EC 0.22 0.37 0.23 0.22 0.42 1.00

pH2 -0.24 0.50 0.37 0.38 0.81 0.74 0.55 0.76 0.22 1.00

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Table 2. Significant linear correlations between soil and grain nutrient concentrations for 77 commercial rice crops.

Fe Mn Zn Ca Mg K P S Na SoilP SoilK SoilMg Soil Ca

Soil Na

Total CEC pH1 EC pH2

Fe 1.00

Mn 1.00

Zn 1.00

Ca 0.37 0.52 1.00

Mg 0.27 0.72 0.53 1.00

K 0.40 0.64 0.55 0.83 1.00

P 0.34 0.78 0.54 0.96 0.90 1.00

S 0.23 0.48 0.35 0.41 0.55 0.53 1.00

Na -0.25 -0.23 1.00

SoilP 0.32 1.00

SoilK -0.42 -0.24 -0.26 -0.29 -0.69 1.00

SoilMg -0.31 -0.31 -0.27 -0.53 0.82 1.00

Soil Ca -0.24 0.33 0.70 -0.49 1.00

Soil Na -0.37 -0.22 -0.23 -0.22 -0.77 0.98 0.85 -0.60 1.00

Total CEC -0.23 0.28 0.60 -0.44 0.95 -0.51 1.00

pH1 0.25 0.33 0.88 -0.77 -0.55 0.83 -0.85 0.74 1.00

EC -0.27 -0.27 -0.87 0.85 0.69 -0.73 0.92 -0.62 -0.97 1.00

pH2 -0.28 -0.27 -0.25 -0.30 0.58 0.76 0.59 -0.27 0.51 1.00

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Table 3. Correlation matrix showing relationships (r values) between soil mineral content and grain quality parameters (n=175). Significant values: 0.159 (P=0.05) and 0.208 (P=0.01).

Avail P

AvailK

EC pH pH Ex Al Ex C

Ex M

Ex N

Ex K CEC HI AGWt

%Ste g/pa Grn DW

PI DW

grain grain grain grain grain grain grain grain grain PI PI PI PI PI PI PI PI PI PI PI PI Colw

ll CaCl wate (0.5

)(0.5

)N Fe Mn Zn Ca Mg K P S N Fe Mn B Cu Zn Ca Mg Na K P S

AvailP(Colwell)

1.00

AvailK 0.69 1.00

EC 0.36 0.35 1.00

pH(CaCl) 0.75 0.67 0.35 1.00

pH(water) 0.78 0.64 0.27 0.96 1.00

Ex Al -0.38 -0.39 -0.28 -0.71 -0.71 1.00

Ex Ca 0.71 0.71 0.37 0.83 0.81 -0.55 1.00

Ex Mg 0.56 0.53 0.25 0.74 0.70 -0.54 0.89 1.00

Ex Na 0.66 0.56 0.58 0.63 0.63 -0.38 0.58 0.25 1.00

Ex K 0.53 0.56 0.17 0.46 0.48 -0.21 0.46 0.10 0.85 1.00

CEC 0.74 0.71 0.42 0.85 0.82 -0.55 0.99 0.90 0.61 0.47 1.00

HI 0.06 0.09 0.14 0.10 0.09 -0.14 -0.06 -0.06 0.07 0.03 -0.04 1.00

AGWt -0.02 0.10 -0.04 -0.17 -0.18 0.27 0.09 0.13 -0.13 -0.04 0.08 -0.08 1.00

%Ster -0.26 -0.22 -0.20 -0.34 -0.31 0.34 -0.25 -0.34 -0.13 -0.02 -0.28 -0.24 -0.16 1.00

g/pan -0.08 -0.01 0.00 -0.02 -0.03 -0.02 -0.16 -0.22 -0.06 0.01 -0.18 0.50 -0.12 -0.26 1.00

DW(0.5msq) 0.09 -0.01 -0.22 -0.07 -0.06 0.25 0.16 0.16 -0.03 0.09 0.14 -0.39 0.21 0.17 -0.30 1.00

PI Dwt(0 5msq)

0.13 -0.09 -0.19 -0.16 -0.13 0.31 0.17 0.26 -0.11 0.01 0.17 -0.34 0.46 0.04 -0.35 0.62 1.00

grain N -0.11 -0.05 -0.13 -0.16 -0.14 0.26 -0.07 -0.17 -0.03 0.05 -0.10 -0.20 -0.18 0.70 -0.31 0.22 0.01 1.00

GrainFe 0.06 0.09 0.14 0.05 0.02 0.07 0.07 0.02 0.09 0.08 0.07 0.02 -0.01 0.03 0.12 -0.07 -0.20 0.21 1.00

GrainMn -0.37 -0.37 -0.29 -0.62 -0.60 0.75 -0.35 -0.37 -0.31 -0.13 -0.37 -0.26 0.24 0.44 -0.18 0.35 0.48 0.39 0.07 1.00

GrainZn -0.07 0.04 0.00 -0.09 -0.10 0.15 -0.03 -0.10 0.01 0.07 -0.04 -0.04 0.07 0.12 0.07 -0.04 -0.14 0.18 0.48 0.10 1.00

GrainCa -0.31 -0.30 -0.12 -0.45 -0.44 0.48 -0.20 -0.17 -0.23 -0.12 -0.21 -0.21 0.27 0.15 -0.04 0.34 0.41 0.08 0.24 0.57 0.28 1.00

GrainMg 0.26 0.18 0.01 0.15 0.15 -0.04 0.21 0.19 0.16 0.22 0.22 0.05 0.18 -0.38 0.31 -0.01 0.19 -0.35 0.14 -0.04 0.09 0.22 1.00

GrainK 0.10 0.09 -0.09 0.10 0.10 -0.12 -0.08 -0.08 0.01 0.06 -0.07 0.21 -0.16 -0.18 0.38 -0.21 -0.19 -0.14 0.08 -0.15 -0.01 -0.14 0.60 1.00

GrainP 0.17 0.12 -0.04 0.06 0.06 0.03 0.10 0.08 0.07 0.14 0.11 0.03 0.13 -0.26 0.25 -0.01 0.18 -0.17 0.06 0.00 0.05 0.15 0.91 0.71 1.00

GrainS 0.00 0.05 -0.07 -0.03 -0.03 0.20 0.05 -0.10 0.10 0.15 0.02 -0.15 -0.11 0.58 -0.21 0.18 -0.02 0.91 0.24 0.32 0.23 0.05 -0.20 -0.02 0.01 1.00

PI N 0.15 0.14 0.17 0.24 0.24 -0.18 0.12 0.03 0.23 0.15 0.12 0.08 -0.42 0.18 0.08 -0.15 -0.34 0.28 0.22 -0.23 0.06 -0.21 -0.23 -0.17 -0.26 0.15 1.00

PI Fe -0.46 -0.23 -0.20 -0.48 -0.49 0.37 -0.43 -0.45 -0.31 -0.16 -0.45 0.01 0.20 0.12 0.22 -0.04 -0.08 0.05 -0.11 0.36 0.03 0.21 -0.01 0.02 0.09 0.07 -0.16 1.00

PI Mn 0.12 -0.01 -0.11 -0.15 -0.14 0.38 0.13 0.14 -0.02 0.07 0.13 -0.22 0.33 0.04 -0.22 0.49 0.75 0.02 -0.16 0.47 -0.07 0.31 0.21 -0.10 0.23 0.04 -0.28 0.08 1.00

PI B -0.07 -0.05 0.11 0.13 0.12 -0.38 -0.18 -0.12 0.02 -0.10 -0.15 0.23 -0.44 -0.10 0.18 -0.42 -0.49 -0.14 0.00 -0.51 0.01 -0.43 -0.14 0.26 -0.07 -0.12 0.16 -0.07 -0.42 1.00

PI Cu 0.35 0.28 0.31 0.25 0.20 0.00 0.43 0.36 0.31 0.23 0.44 -0.13 0.09 0.29 -0.11 0.23 0.33 0.16 0.01 0.03 0.19 0.01 -0.24 -0.44 -0.29 0.20 0.23 -0.18 0.30 -0.28 1.00

PI Zn -0.44 -0.42 0.04 -0.51 -0.51 0.42 -0.39 -0.39 -0.24 -0.26 -0.40 -0.18 -0.03 0.41 -0.19 0.12 0.09 0.37 0.05 0.47 0.03 0.31 -0.33 -0.33 -0.26 0.20 0.36 0.16 0.07 -0.10 0.18 1.00

PI Ca -0.23 -0.09 0.35 -0.03 -0.10 -0.16 -0.13 -0.10 0.05 -0.16 -0.10 0.03 -0.26 -0.15 -0.03 -0.24 -0.42 -0.19 -0.01 -0.20 0.05 -0.03 -0.18 0.00 -0.12 -0.10 -0.16 0.18 -0.17 0.35 -0.15 -0.01 1.00

PI Mg 0.42 0.22 0.32 0.65 0.64 -0.62 0.28 0.31 0.42 0.18 0.34 0.32 -0.47 -0.24 0.14 -0.35 -0.42 -0.18 0.03 -0.69 -0.11 -0.52 -0.09 0.19 -0.09 -0.11 0.31 -0.35 -0.33 0.65 0.05 -0.28 0.26 1.00

PI Na 0.30 0.06 0.30 0.54 0.54 -0.52 0.20 0.25 0.36 0.11 0.26 0.31 -0.34 -0.19 0.14 -0.37 -0.34 -0.15 -0.01 -0.53 -0.13 -0.42 -0.06 0.16 -0.08 -0.11 0.19 -0.21 -0.35 0.56 0.00 -0.29 0.13 0.81 1.00

PI K -0.10 0.08 -0.33 -0.31 -0.29 0.39 -0.15 -0.26 -0.19 0.07 -0.20 -0.11 0.12 0.31 0.03 0.28 0.20 0.38 0.05 0.35 0.05 0.13 0.04 0.01 0.12 0.31 0.25 0.10 0.18 -0.29 0.09 0.40 -0.49 -0.45 -0.64 1.00

PI P 0.30 0.13 -0.04 -0.05 -0.06 0.36 0.22 0.17 0.09 0.22 0.22 -0.08 0.34 0.05 -0.14 0.50 0.67 0.11 0.00 0.39 -0.05 0.28 0.27 -0.04 0.31 0.16 -0.10 -0.06 0.59 -0.40 0.36 0.19 -0.46 -0.26 -0.38 0.47 1.00

PI S 0.11 0.11 0.21 0.14 0.11 0.00 0.09 -0.01 0.21 0.12 0.10 0.00 -0.29 0.30 -0.03 -0.06 -0.26 0.38 0.23 -0.06 0.06 -0.14 -0.30 -0.26 -0.29 0.32 0.86 -0.07 -0.10 0.13 0.36 0.49 -0.11 0.23 0.12 0.31 0.06 1.0

PI Al -0.18 -0.01 -0.11 -0.13 -0.15 0.16 -0.19 -0.22 -0.12 -0.04 -0.20 0.07 0.05 -0.03 0.24 -0.13 -0.22 -0.05 -0.11 0.07 -0.10 -0.03 -0.04 0.15 0.02 -0.04 0.01 0.82 -0.03 -0.05 -0.13 0.04 0.15 -0.08 -0.02 0.00 -0.14 0.0

HI=harvest index, AvGWt=average grain weight, %ster=percent sterility, g/pan=average number of grains per panicle).

10

Table 4a: Concentrations of minerals in brown rice grain at 14% moisture (mg/kg) Variety N S P K Mg Ca Cu Fe Mn Na Zn Amaroo 10922 909 2814 2562 1136 99 4 13 44 72 16 Doongara 12126 998 3357 2874 1276 110 4 12 43 25 21 Illabong 12126 1003 3095 2833 1270 119 4 13 50 52 19 Jarrah 11610 940 2916 2735 1182 103 4 14 49 58 18 Koshi 11954 947 2734 2502 1130 106 3 14 39 41 18 Kyeema 10320 819 2681 2385 1153 93 3 14 31 36 17 Langi 12126 949 3309 2805 1250 110 4 12 46 25 21 E. Millin 12126 992 3353 2826 1262 114 6 12 46 28 21 Millin 11696 933 2821 2562 1147 105 3 16 47 47 18 Namaga 11008 855 2567 2360 1063 102 4 11 37 43 16 Opus 10922 883 2608 2367 1071 103 5 13 42 79 16 AVERAGE 11540 930 2932 2619 1176 106 4 13 43 46 18 Table 4b: Mean nutrient concentrations in paddy rice at 14% moisture

N S P K Mg Ca Cu Fe Mn Na Zn Kg / tonne 10 0.8 2.5 3.1 1.0 0.2 0.004 0.025 0.096 0.071 0.018

Kg / 9.3 tonne 93 7.7 23.1 29.1 9.7 2.2 0.033 0.233 0.891 0.664 0.166 Kg / 12 tonne 120 9.9 29.8 37.5 12.6 2.8 0.043 0.301 1.150 0.857 0.215

11

4.2 Grain mineral / grain quality study In the first year (Figure 1a), an r2 of 0.41 was found between the Mg/K ratio and peak viscosity (significant at P=0.01). In the second year, there was no relationship found for Amaroo (Figure 2a), and the strongest correlation was found for cv. Millin (r2=0.51, not shown). In the third year (Figure 3a), there was a significant correlation between the Mg/K ratio and peak viscosity (r2=0.47, significant at P=0.01). There was a significant negative correlation between peak viscosity and protein concentration in the 1992-93 (P=0.05) and 1996-97 (P=0.01) seasons (Figures 1b and 2b). There was no correlation between protein and peak viscosity in the 1998-99 season (Figure 3b). There was a trend in the 1998-99 Amaroo samples for the RVA traces of grain from the Murray Valley to have a higher peak viscosity than grain from the Murrumbidgee Irrigation Area (Figure 4). The trend is not evident in other varieties. This suggests there may be a genotype x environment interaction for Amaroo that requires further investigation. Figure 1: Peak viscosity vs the Mg/K ratio (a) and protein (b) for the cv. Amaroo (1992-93). Figure 1a. Figure 1b.

Figure 2: Peak viscosity vs the Mg/K ratio (a) and protein (b) for the cv. Amaroo (1996-97). Figure 2a. Figure 2b.

Figure 3: Peak viscosity vs Mg/K ratio (a) and protein (b) for the cv. Amaroo (1998-99). Figure 3a. Figure 3b.

R2 = 0.41

150

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1.2 1.4 1.6Mg/K ratio

Pea

k vi

scos

ity (R

VU)

R2 = 0.02

150

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Peak

vis

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ty (R

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R2 = 0.47

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1.2 1.3 1.4 1.5 1.6 1.7Mg/K ratio

Pea

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)

R2 = 7E-05

200

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6.00 7.00 8.00 9.00%Protein

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)

R2 = 0.-3447

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6 7 8 9 10Protein (%)

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vis

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ty (R

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R2 = -0.24

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6 7 8 9Protein (%)

Peak

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12

Figure 4: Viscosity of Amaroo grain samples from the major rice areas (1998-99 season).

00

60

120

180

240

00 3 6 9 12 1515

Time mins

Viscosity RVU

---------------Murray Valley---------------Murray Valley and MIA---------------MIA

4.3 Variation in shoot and grain mineral concentrations within and

between rice paddocks Tables 5-9 show the maximum, minimum, mean and standard deviation values for the mineral concentrations in grain, straw and shoot samples from 50 sampling sites in each of four commercial crops. Shoot samples were collected at mid-tillering, PI and flowering. The variability of grain mineral concentrations between paddocks is shown graphically in Figures 5-8. The CV or variation in minerals across a paddock is the Standard Deviation divided by the mean and converted to a percentage. The CVs for grain minerals ranged from as low as 3 to 10% for macro elements such as P, K and Mg, to 7 to 20% for Nitrogen and some micro elements but ranged from 7 to 29% for sodium. The CV for minerals in shoots collected at PI were consistently larger than the corresponding values for grain. But sodium was very variable with CV’s as large as 107%.

13

Table 5: Mean and range of grain mineral concentrations (mg/kg) in four paddocks (1998-99 season).

N(%) P K S Mg Ca Fe Mn Na Zn Paddock MG

Max 1.70 3700 3200 1370 1380 148 12.4 35 220 24 Min 0.95 2700 2400 830 1130 80 8.2 21 17 14 Mean 1.19 3034 2677 1010 1221 95 10.1 26 54 18 Stdev 0.17 148 128 102 54 11 1.0 2.9 49 2.3 Paddock RW

Max 1.82 3400 3100 1270 1320 119 15.6 59 67 53 Min 1.01 1990 1890 920 800 84 8.3 25 16 16 Mean 1.27 2974 2753 1057 1165 103 10.5 37 23 20 Stdev 0.20 263 239 87 92 8 1.5 7.4 10 5.8 Paddock SM

Max 1.34 3200 3000 1160 1300 119 14.6 30 37 21 Min 0.89 2600 2300 840 1050 80 8.2 19 15 16 Mean 1.07 2961 2789 954 1180 96 10.2 23 20 18 Stdev 0.09 121 132 55 49 6 1.1 2.8 6 1.2 Paddock WH

Max 1.23 3100 3100 1050 1270 123 14.1 63 61 22 Min 0.98 2900 2400 890 1140 102 7.1 31 15 15 Mean 1.10 3008 2629 955 1204 111 9.9 42 22 18 Stdev 0.08 72 140 52 30 5 1.2 7.4 15 1.4

Table 6. Mean and range of mineral concentrations (mg/kg) in straw in four paddocks (1998-99 season).

N (%) P K S Mg Ca Fe Mn Na Zn Paddock MG

Max 1.16 1210 26000 1320 2100 3300 460 680 16100 23 Min 0.44 410 3700 600 990 1900 78 132 350 8.4 Mean 0.69 790 16751 824 1396 2451 182 384 4002 15 Stdev 0.15 191 4584 149 261 310 69 104 4017 2.9 Paddock RW

Max 1.42 1260 26000 1230 1970 3200 1010 1470 3200 41 Min 0.49 230 16800 600 750 1890 130 300 410 18 Mean 0.81 737 20071 874 1147 2613 379 633 1080 26 Stdev 0.20 216 2169 170 235 259 216 229 488 4.8 Paddock SM

Max 0.90 1090 20000 1040 1670 3600 1270 570 3500 54 Min 0.49 410 14000 550 840 2200 100 164 540 14 Mean 0.64 682 16965 732 1175 2758 349 322 1424 20 Stdev 0.09 133 1689 103 205 272 276 90 752 6.0 Paddock WH

Max 0.78 1160 21000 790 1240 3100 640 1080 900 22 Min 0.47 580 12200 530 880 2100 87 410 145 12 Mean 0.62 858 17675 648 1014 2542 319 675 388 17 Stdev 0.09 157 2126 81 102 269 152 144 200 3.0

14

Table 7. Mean and range of shoot mineral concentrations (mg/kg) at mid-tillering in four paddocks (1998-99 season).


Max 4.55 5100 39000 3900 2900 2900 720 890 13500 26.5 Min 2.75 3500 15000 2500 1860 1540 169 330 2300 14.2 Mean 3.85 4490 31390 2998 2356 2042 348 614 4951 20.5 Stdev 0.37 383 6472 244 238 270 139 119 2960 2.8 Paddock RW

Max 4.96 4600 37000 3300 2100 2500 2000 1100 4400 38.2 Min 2.88 2600 27000 2000 1470 1560 360 240 1500 24.3 Mean 4.06 3463 33217 2883 1750 2051 809 540 2841 30.6 Stdev 0.38 517 2467 273 141 222 362 211 830 2.9 Paddock SM

Max 4.78 4200 39000 3800 2500 2700 580 590 8600 44.8 Min 3.21 2800 26000 2400 1620 1500 191 176 2700 20.0 Mean 4.01 3402 32959 3124 2069 2052 293 288 4780 32.2 Stdev 0.36 305 2590 392 202 288 85 77 1282 5.9 Paddock WH

Max 4.27 3900 34000 2600 1890 2700 340 460 1480 34.4 Min 2.52 2800 27000 2100 1540 1820 170 180 540 20.0 Mean 3.43 3482 32364 2364 1695 2247 232 348 927 28.1 Stdev 0.45 371 2014 150 101 263 46 72 349 3.8

Table 8. Mean and range of shoot mineral concentrations (mg/kg) in shoots at PI in four paddocks (1998-99 season).






15

Table 9. Mean and range of shoot mineral concentrations (mg/kg) at flowering in paddocks (1998-99 season).






Figure 5. Mean and range of grain mineral concentrations in 4 different paddocks (macro elements).

Mean grain mineral concentration in 4 paddocks (macro elements)

0

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Paddock MG Paddock RW Paddock SM Paddock WH

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n (m

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) exc

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/kg) N

PKSMg

16

Figure 6. Mean and range of grain mineral concentrations in 4 different paddocks (micro elements).

Figure 7. Mean and range of shoot mineral concentrations in 4 different paddocks (macro elements).

Mean grain mineral concentration in 4 paddocks (micro elements)

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Mean shoot mineral concentration in 4 paddocks (macro elements)

0500

1000150020002500300035004000

Paddock SM Paddock RW Paddock MG Paddock WH

Min

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Figure 8. Mean and range of shoot mineral concentrations in 4 different paddocks (micro elements).

Mean shoot mineral concentration in 4 paddocks (micro elements)

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18

4.3.1 Ability to detect mineral variation by remotely sensed images Figure 9a and b provides graphical evidence that there is a link between the soil-adjusted vegetation index (SAVI) image of a paddock at mid season (indicating biomass variation throughout the paddock) and a minimum curvature representative of the soil P based on 50 samples from the paddock. Figure 9a Simple ratio (SR) image of a paddock at PI, indicating biomass variation throughout the paddock. Figure 9b: A minimum curvature surface created using 50 soil P measurements for the above paddock. The remotely sensed image shows visual representation with the map created based on the soil P samples. For example, at the bottom right of the image, slightly in from the edge on both images the area is dark, whilst directly above this position at the top of both images there is a corresponding lighter area. Therefore based on this simple visual comparison there seems to be potential to extract areas of high and low soil P from an image taken at PI.

422600.00 422700.00 422800.00 422900.00 423000.00 423100.00

6215900.00

6216000.00

6216100.00

422600.00 422700.00 422800.00 422900.00 423000.00 423100.00

6215900.00

6216000.00

6216100.00

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4.4 Critical nutrient concentrations in whole rice shoots to provide a

basis for diagnosing problem crops The concentrations of nutrients in grains collected from a wide range of commercial crops in 1997/98 and 198/99 are presented in Table 8. There is a lack of experimental data, for most elements other than nitrogen, to allow grain yields in Australia to be related to mineral concentrations in grain. The concentrations presented here may be regarded as preliminary estimates of adequate concentrations of nutrients for rice. Current studies by Yash Dang (INCITEC Ltd) may provide better estimates of the critical P and Zn concentrations for rice. 4.5 A Nutrient balance for an “average” Australian rice crop Data obtained during the current project, together with data collected during previous RIRDC Projects and other sources, made it possible to prepare a nutrient balance sheet for an “average” rice crop. The following sets of data were used to estimate nutrient balances shown in Table 10. Nutrient inputs and removals were determined for the crop grown in the 1998–1999 season, which achieved an industry average yield of 9.3 tonne /ha (at 14% moisture content). Nutrients applied as fertilizer were calculated from information supplied by rice growers who used the NIR Tissue Testing Service operated by Ricegrowers Co–operative Limited (Blakeney et al. 1994; Batten et al. 2000). This service is used by over 40% of rice producers. Average concentrations of nutrients in irrigation water used for the summers of 1998 and 1999 at the Narrandera regulator and the Sturt Canal offtake were supplied by Murrumbidgee Irrigation Limited, Leeton. The average use of irrigation water was 13.3 ML/ha (data supplied by Murray and Murrumbidgee Irrigation Limited). Nutrients in grain and stubble were provided from published data for Australian rice crops (Marr et al. 1995; 1999). Nutrients input in seed were calculated using seeding rate data taken from RiceCheck records for the 1998–1999 crop (J Lacy personal communication). Losses due to stubble burning were estimated using data summarised by Kirkby (1999). Losses of applied N fertilizer via denitrification and ammonia volatilisation were estimated from Australian research (Bacon and Heenan 1987; Simpson et al. 1988) as 35 percent of the level of N applied. HOM This budget suggests the following

- Despite the high inputs of nitrogen fertilizer there is a net loss of N from the system - Phosphorus inputs are low compared to the exports in grain - Large losses of potassium occur, especially when stubbles are burnt, but no potassium

fertilizers are used on rice - irrigation water supplies significant amounts of sulphur, calcium and magnesium - stubble burning is a major cause of nutrient losses from the system.

20

Table 10. Nutrient balances (kg/ha) for rice grown using average industry inputs of irrigation water (13.3 ML/ha) and fertilizers to produce an average yield of 9.3 t grain/ha).

Mineral N S P K Mg Ca Cu Fe Mn Na Zn INPUTS SEED 1.50 0.12 0.37 0.47 0.16 0.04 0.001 0.004 0.014 0.011 0.003 FERTILISER 120 3.5 4.6 0 0 3.6 0 0 0 0 0.015 IRRIGATION WATER

4.6 18 0.67 3.86 15.3 24.5 * * * 42.4 *

Total Inputs 126.1 21.6 5.6 4.3 15.5 28.1 0.001 0.004 0.014 42.4 0.018

EXPORT GRAIN 93 7.7 23.1 29.1 9.7 2.2 0.033 0.233 0.891 0.664 0.166

STUBBLE BURNT 57 5.4 2.4 97 8 13.5 * * * * * N losses 42 BALANCE STUBBLE INCORPORATED

-9.0 13.9 -17.4 -24.7 5.7 25.9 -0.033 -0.229 -0.877 41.7 -0.149

STUBBLE BURNT -66.0 8.5 -19.8 -121.7 -2.3 12.4 * * * * *

*insufficient data

21

5. Implications The concentrations of minerals in rice reflect in part the chemical constituents of the soil on which it is grown. The correlations reported here across farms are, generally, not strong but indicate that the decline in soil P status (predicted from the nutrient budget Table 10 and unpublished data by Dr H Gill at Yanco) may lead to lower N, P Mg and some trace elements but increased Zn and Al in shoots; reduced P and Mg, but higher Mn and Ca in grain. The decline in soil pH reported by Lake and Beecher (pers. comm.) can be expected to lead to lower grain yields with higher N, Mn and Ca concentrations – possibly a reduction in cooking quality. Increases in the soil EC may lead to higher Mn in grain. Although in samples from some years we have found reasonably strong relationships between the Mg/K ration of brown Amaroo grain and cooking quality, there is not enough evidence to date to conclude that the Mg/K ratio of brown grain significantly affects the cooking quality of the white rice grain. There is also not enough known about how the Mg/K ratio of brown grain would affect the starch properties or cooking qualities of white rice. The ability to map mineral nutrient concentrations within a paddock using airborne video images may have implications for understanding the impact of the variability of minerals in a rice paddock on yield and grain quality. Further examination of these data is in progress to determine the implications of cut-fill patterns and whether variable mineral fertilisation would be a viable option. This project has enabled data to be assembled to produce nutrient balances for the rice crop portion of a rice-based farming system. While N fertility is well understood and plant tests are in place to monitor N fertility, the balance sheet indicates the need for additional P and Zn to many rice crops now, and the possible need for additional K inputs in the future. Other micro-nutrients should also be monitored. This work provides a sound basis to rank the order of importance of future nutrient studies.

6. Recommendations The growth, nutrient uptake and grain quality of rice grown on soils with low P, low pH and high salinity require further study to assess the impact of these soil stresses. Soil testing is recommended to monitor declines in soil pH, P and other nutrients. Further studies of the soil variability within and between paddocks to better understand its impact on crop yields, and grain quality is warranted. There is a need to understand the cause of varietal variations in mineral uptake and utilization. Glass house or detached panicle studies should be used to test the hypothesis that the Mg/K ratio in grain in implicated in cooking quality.

22

7. References Bacon, P. and Heenan, D. (1987) Nitrogen budgets for intensive rice growing in Australia. In: Efficiency of nitrogen fertilizers for rice. (S. Banta. ed.), pp 89-95. International Rice Research Institute, Los Banos, Philippines Barrs HD and Prathapar SA. (1994). An inexpensive and effective basis for monitoring rice areas using GIS and remote sensing. Australian Journal of Experimental Agriculture 34; 7, 1079-1083. Batten, GD, Blakeney, AB and Ciavarella, S. (1994). A tissue testing service for rice producers. pp 473-5. In E Humphreys, EA Murray, WS Clampett and LG Lewin (Eds), “Temperate Rice : achievements and potential” NSW Agriculture: Griffith. Batten, G.D., Blakeney, A.B. and Ciavarella, S. (2000) NIR for improved fertilizer predictions: update 2000. IREC Farmers™ Newsletter (Large Area) 154: 36Œ3 Blakeney, A.B., Batten G.D. and Ciavarella, S. (1994). An interactive database for use with the rice tissue test service. pp 477-84. In E Humphreys, EA Murray, WS Clampett and LG Lewin (Eds), “Temperate Rice : achievements and potential” NSW Agriculture: Griffith. Blakeney, AB, Batten, GD and Ciavarella, S. (1994). An interactive database for use with the rice tissue testing service. In: ‘Proceedings of the 1994 Temperate Rice Conference’. February 1994, Yanco, NSW. (Eds E. Humphreys, EA Murray, WS Clampett and LG Lewin). 473-5. (Temperate Rice Organising Committee:NSW Agriculture, Griffith). Boerema, EB. (1974). Growth and yield of rice in the Murrumbidgee Valley as influenced by climate, method of sowing, plant density and nitrogen nutrition. MSc Thesis, Macquarie University, NSW. Elding, ME. 1968, The Dumas method for nitrogen in feeds. Journal of the Association of Official Analytical Chemists, 51, 766 Huang, J-F. (1990). The relation between soil nutrients and rice qualities. Transactions 14th International Congress of Soil Science, Kyoto, Japan, August 1990. Vol IV, p. 170-175. Louis, J., Lamb, D., McKenzie, G., Chapman, G., Edirisinghe, A., McLeod, I. and Pratley, J. 1995, Operational use and calibration of airborne video imagery for agricultural and environmental land management applications. In 15th Biennial Workshop on Videography and Color Photography in Resource Management. American Society for Photogrammetry and Remote Sensing, Indiana, USA, May 1-3, 326-333. Lacy J., Clampett W., Lewin L., Reinke R., Batten G., Williams R., Beale P., McCaffery D., Lattimore M., Schipp A., Salvestro R. and Nagy J. (2000). ‘2000 Ricecheck Recommendations,’ NSW Agriculture and RIRDC. Kirkby CA (1999) Survey of current rice stubble management practices for identification of research needs and future policy. Draft RIRDC report Marr KM, Batten, GD and Blakeney, AB (1995). Relationships between minerals in Australian brown rice. Journal of the Science of Food and Agriculture 68, 285-91.

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Marr, KM, Batten, GD and Lewin, LG. (1999). The effect of nitrogen fertiliser on yield, nitrogen and mineral elements in Australian brown rice. Australian Journal of Experimental Agriculture 39, 873-80. Peoples, MB. Bowman, AM. Gault, RR. Herridge, DF. McCallum, MH. McCormick,, KM. Norton, RM. Rochester, IJ. Scammell, GJ, and Schwenke, GD. (2000) Factors regulating the contributions of fixed nitrogen by pasture and crop legumes to different farming systems of eastern Australia. Plant and Soil 228: 29Œ41. Simpson, J., Muirhead, W., Bowmer, K., Cai, G. and Freney, J. (1988) Control of gaseous nitrogen losses from urea applied to flooded rice soils. Fertilizer Research 18: 31Œ47. Smith SM., Schrier H. and Wiart R. (1987). Agricultural field management with micro-computer based GIS and image analysis systems. Second Annual International Conference, Exhibits and Workshops on Geographic Information Systems. Vol 2, 585-594. Spackman, SL., Lamb, DW. and Louis, J. (2000). Using airborne multispectral imagery to manage within-field variability in rice production. Aspects of Applied Biology 60. 99-106. Zarcinas, BA, Cartwright B and Spouncer LR (1987). Nitric acid digestion and multielement analysis of plant material by inductively coupled plasma spectroscopy. Communications in Soil Science and Plant Analysis 20 (5-6) 539-553.

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8. Publications List Peer reviewed articles Allan, A.M., Blakeney, A.B., Batten, G.D. and Dunn, T.S. (1999). Impact of grinder

configurations on grinding rate, particle size and trace element contamination of plant samples. Communications in Soil Science and Plant Analysis, 30 (15-16),2123-2135

Batten, G.D., Marr, K.M., Williams, R.L and Farrell, T.C (2000). Mineral concentrations in

Australian and overseas brown rice genotypes. Communications in Soil Science and Plant Analysis, 31 (11-14), 2393-2400.

Lott, J.N.A., Ockenden, I, Raboy, V. and Batten, G.D. (2000) Phytic acid and phosphorus in crop

seeds and fruits: A global estimate. Seed Science Research 10, 11-33. Marr, KM, Batten, GD and Lewin, LG. (1999). The effect of nitrogen fertiliser on yield, nitrogen

and mineral elements in Australian brown rice. Australian Journal of Experimental Agriculture 39, 873-80.

Rengel, Z., Batten, G.D and Crowley, D.E. (1999) Agronomic approaches for improving the

micronutrient density in edible portions of field crops. Special Issue of Field Crops Research 60, 27 – 40.

Conference papers Allan, A.M., Blakeney, A.B., Batten,G.D and Dunn, T.S. (1998). Grinding plant samples: rate,

recovery and particle size. 8th Australian Near Infrared Spectroscopy Conference, Palm Cove 21-22 August 1998

Lewin, L.G., Batten, G.D., Blakeney, A.B., Reinke, R.F., Williams, R.L. and Fitzgerald, M.A.

(1998). Genetic improvement of rice in Australia – a key factor in sustainable rice production. International Symposium on Rice Germplasm, Evaluation and Enhancement. National Rice Germplasm Evaluation and Enhancement Centre USDA-ARS and Rice Research and Extension Centre, Divn of Agriculture University of Arkansas, 30th August – 2nd September, 1998.

Batten, G.D., Marr, K.M., Williams, R.L and Farrell, T.C (1999). Mineral concentrations in brown

rice in relation to grain:shoot dry matter ratio. 6th International Symposium on Soil and Plant Nutrition, Brisbane 22-26 March 1999.

Clampett, W.S., Lewin, L.G., Williams, R.L., Batten, G.D., Beecher, H.G, Lacy, J.M., Fitzgerald,

M. and Stevens, M (1999). An overview of temperate rice production, technology and development in New South Wales, Australia. Paper presented at the 2nd Temperate Rice Conference, Sacramento, California 13-18 June 1999.

Batten, GD , Reuter, D., Unkovich , M., and Kirkby, C (2001) A preliminary nutrient audit of the

Australian rice industry. 10th Australian Agronomy Conference, Hobart Tasmania, 28th Jan – 1st Feb 2001.

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Batten, G., Marr, K. and Blakeney, A. (1998) Defining mineral requirements for yield and quality. Farmers’ Newsletter 152, 42 –43.

Lacy, J., Clampett, W., Lewin, L., Reinke, R., Batten, G., Williams, R., Beale, P., McCaffery, D.

and Lattimore, M., Schipp, A.,Nagy, J. and Salvestro, R. (1999) "1999 Ricecheck Recommendations". NSW Agriculture. pp20.

Batten, G., Marr, K., Gill, H. and Fitzgerald, M. (2000) Determining soil minerals role in rice

quantity and quality. IREC Farmers’ Newsletter 154, 38-39. Batten, G, Blakeney, and Ciavarella, S. (2001) NIR for improved fertilizer predictions: update

2000. IREC Farmers’ Newsletter 156, 40-41. Batten, G., Marr, K., Gill, H. and Fitzgerald, M. (2001) What influences mineral elements in rice

grain. IREC Farmers’ Newsletter 156, 36-38. Seminars / lectures by G Batten “Plant nutrition, productivity and grain quality”. Seminar to staff of Yanco Agricultural Institute. 19th November 1999. What I do when asked by a producer to “come and look at my crop”. Audience: Year 4 Crop Science Students, University of Sydney. 20th March 2000 Plant disorder diagnosis session. Audience: Year 4 Crop Science Students, University of Sydney. 21st March 2000.

Documents

Relating minerals in rice shoots and grain to soil tests, yield and grain quality · Relating minerals in rice shoots and grain to soil tests, yield and grain quality Publication