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
© 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 Fax Email
02 6933 4207 02 6933 2812
[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: Fax: Email: Website:
02 6272 4539 02 6272 5877
[email protected]. http://www.rirdc.gov.au
Published in August 2002 Printed on environmentally friendly paper by Canprint
ii
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
iii
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
iv
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 3.1.2 3.1.3
Grain mineral / grain quality studies............................................................................... 3 Variation in grain minerals within a paddock................................................................. 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
v
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. vi
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.
vii
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.
1
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.
2
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 subsamples 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).
3
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).
5
- 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 Fe
Mn
Zn
Ca
Mg
P
S
Al
Soil P
Soil K Soil Ca
Soil Mg
Soil Na
Tot CEC
0.22
EC
pH2
1.00 1.00
Mg
0.28
Na
1.00 -0.21
-0.34
0.36
K P
0.21
S
0.39
1.00 -0.40
1.00
-0.24
0.47
0.29
0.49
1.00 1.00
7
0.70
1.00
Soil P
0.22
Soil K
-0.23
-0.28
-0.23
-0.29
Soil Mg
-0.23
-0.43 -0.30
Soil Na -0.21
0.25
0.47
1.00
0.26
Soil Ca
-0.37
0.27
0.31
0.24
-0.24
-0.25
0.49
0.53
-0.32
-0.30
0.53
0.74
0.42
0.43
-0.25
-0.38
1.00 0.49
1.00
0.35
0.82
1.00
0.43
0.77
1.00
0.88
0.94
0.67
-0.28 -0.26
0.40
1.00
pH1
1.00
EC pH2
pH1
1.00
Ca
Tot CEC
K
1.00
B
Al
Na
1.00
Mn
Zn
B
-0.24
0.22
0.37
0.50
0.37
0.23 0.38
0.81
0.22
0.42
0.74
0.55
1.00 0.76
0.22
1.00
Table 2. Significant linear correlations between soil and grain nutrient concentrations for 77 commercial rice crops.
Fe Fe Mn
Mn
Zn
Ca
Mg
K
P
SoilP
SoilK SoilMg
Soil Ca
Soil Na
Total CEC
pH1
EC
1.00 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
Na
-0.25
-0.23
SoilP
1.00 1.00 0.32
8
SoilK
-0.42
SoilMg
-0.31
-0.24
-0.37
1.00
-0.26
-0.29
-0.69
1.00
-0.31
-0.27
-0.53
0.82
0.33
0.70
-0.49
-0.22
-0.77
0.98
0.28
0.60
-0.44
-0.24
Soil Na
-0.22
-0.23
-0.23
1.00 1.00 0.85
-0.60
1.00
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
-0.30
0.58
0.76
-0.27
0.51
pH2
pH2
1.00
Ca
Total CEC
Na
1.00
Zn
Soil Ca
S
-0.28
-0.27
-0.25
0.59
1.00
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).
AvailP(Colwe ll) AvailK
Avail P Colw ll 1.00
Avail K
0.69
1.00
EC
pH
pH
CaCl
wate
Ex Al
Ex C
Ex M
Ex N
Ex K
CEC
HI
AGW %Ste t
g/pa
Grn DW (0.5 )
PI DW (0.5 )
grain
grain
grain
grain
grain
grain
grain
grain
grain
PI
PI
PI
PI
PI
PI
PI
PI
PI
PI
PI
PI
N
Fe
Mn
Zn
Ca
Mg
K
P
S
N
Fe
Mn
B
Cu
Zn
Ca
Mg
Na
K
P
S
9
EC
0.36
0.35
1.00
pH(CaCl)
0.75
0.67
0.35
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
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) grain N
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
-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
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
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
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
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
-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
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
PI B
1.00
1.00
1.00
1.00 1.00 1.00 1.00
HI=harvest index, AvGWt=average grain weight, %ster=percent sterility, g/pan=average number of grains per panicle).
1.00
Table 4a: Concentrations of minerals in brown rice grain at 14% moisture (mg/kg)
10
Variety Amaroo Doongara Illabong Jarrah Koshi Kyeema Langi E. Millin Millin Namaga Opus AVERAGE
N 10922 12126 12126 11610 11954 10320 12126 12126 11696 11008 10922 11540
S 909 998 1003 940 947 819 949 992 933 855 883 930
P 2814 3357 3095 2916 2734 2681 3309 3353 2821 2567 2608 2932
K 2562 2874 2833 2735 2502 2385 2805 2826 2562 2360 2367 2619
Mg 1136 1276 1270 1182 1130 1153 1250 1262 1147 1063 1071 1176
Ca 99 110 119 103 106 93 110 114 105 102 103 106
Cu 4 4 4 4 3 3 4 6 3 4 5 4
Fe 13 12 13 14 14 14 12 12 16 11 13 13
Mn 44 43 50 49 39 31 46 46 47 37 42 43
Na 0.071 0.664 0.857
Zn 0.018 0.166 0.215
Table 4b: Mean nutrient concentrations in paddy rice at 14% moisture
Kg / tonne Kg / 9.3 tonne Kg / 12 tonne
N 10 93 120
S 0.8 7.7 9.9
P 2.5 23.1 29.8
K 3.1 29.1 37.5
Mg 1.0 9.7 12.6
Ca 0.2 2.2 2.8
Cu 0.004 0.033 0.043
Fe 0.025 0.233 0.301
Mn 0.096 0.891 1.150
Na 72 25 52 58 41 36 25 28 47 43 79 46
Zn 16 21 19 18 18 17 21 21 18 16 16 18
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. R2 = -0.24 Peak viscosity
Peak viscosity (RVU)
R2 = 0.41 300 250 200 150 1.2
1.4 Mg/K ratio
300 250 200 150
1.6
6
7 8 Protein (%)
9
Figure 2: Peak viscosity vs the Mg/K ratio (a) and protein (b) for the cv. Amaroo (1996-97). Figure 2a.
Figure 2b.
300
Peak viscosity (RVU)
Peak viscosity (RVU)
R2 = 0.02
250 200 150 1.2
1.4 Mg/K ratio
1.6
R2 = 0.-3447
350 300 250 200 150 6
7
8 Protein (%)
9
10
Figure 3: Peak viscosity vs Mg/K ratio (a) and protein (b) for the cv. Amaroo (1998-99). Figure 3b.
Peak viscosity (RVU)
R2 = 0.47 300 250 200 150 1.2
1.3
1.4 1.5 Mg/K ratio
1.6
1.7
Peak viscosity (RVU)
Figure 3a.
R2 = 7E-05 260 240 220 200 6.00
11
7.00 8.00 %Protein
9.00
Figure 4: Viscosity of Amaroo grain samples from the major rice areas (1998-99 season).
---------------M urray V alley ---------------M urray V alley and M IA
240
Viscosity RVU
---------------M IA
180
120
60
0 0
3
6
9
12
15
Time mins
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%.
12
Table 5: Mean and range of grain mineral concentrations (mg/kg) in four paddocks (1998-99 season). Paddock MG Max Min Mean Stdev Paddock RW Max Min Mean Stdev Paddock SM Max Min Mean Stdev Paddock WH Max Min Mean Stdev
N(%)
P
K
S
Mg
Ca
Fe
Mn
Na
Zn
1.70 0.95 1.19 0.17
3700 2700 3034 148
3200 2400 2677 128
1370 830 1010 102
1380 1130 1221 54
148 80 95 11
12.4 8.2 10.1 1.0
35 21 26 2.9
220 17 54 49
24 14 18 2.3
1.82 1.01 1.27 0.20
3400 1990 2974 263
3100 1890 2753 239
1270 920 1057 87
1320 800 1165 92
119 84 103 8
15.6 8.3 10.5 1.5
59 25 37 7.4
67 16 23 10
53 16 20 5.8
1.34 0.89 1.07 0.09
3200 2600 2961 121
3000 2300 2789 132
1160 840 954 55
1300 1050 1180 49
119 80 96 6
14.6 8.2 10.2 1.1
30 19 23 2.8
37 15 20 6
21 16 18 1.2
1.23 0.98 1.10 0.08
3100 2900 3008 72
3100 2400 2629 140
1050 890 955 52
1270 1140 1204 30
123 102 111 5
14.1 7.1 9.9 1.2
63 31 42 7.4
61 15 22 15
22 15 18 1.4
Table 6. Mean and range of mineral concentrations (mg/kg) in straw in four paddocks (1998-99 season). Paddock MG Max Min Mean Stdev Paddock RW Max Min Mean Stdev Paddock SM Max Min Mean Stdev Paddock WH Max Min Mean Stdev
N (%)
P
K
S
Mg
Ca
Fe
Mn
Na
Zn
1.16 0.44 0.69 0.15
1210 410 790 191
26000 3700 16751 4584
1320 600 824 149
2100 990 1396 261
3300 1900 2451 310
460 78 182 69
680 132 384 104
16100 350 4002 4017
23 8.4 15 2.9
1.42 0.49 0.81 0.20
1260 230 737 216
26000 16800 20071 2169
1230 600 874 170
1970 750 1147 235
3200 1890 2613 259
1010 130 379 216
1470 300 633 229
3200 410 1080 488
41 18 26 4.8
0.90 0.49 0.64 0.09
1090 410 682 133
20000 14000 16965 1689
1040 550 732 103
1670 840 1175 205
3600 2200 2758 272
1270 100 349 276
570 164 322 90
3500 540 1424 752
54 14 20 6.0
0.78 0.47 0.62 0.09
1160 580 858 157
21000 12200 17675 2126
790 530 648 81
1240 880 1014 102
3100 2100 2542 269
640 87 319 152
1080 410 675 144
900 145 388 200
22 12 17 3.0
13
Table 7. Mean and range of shoot mineral concentrations (mg/kg) at mid-tillering in four paddocks (1998-99 season). Paddock MG Max Min Mean Stdev Paddock RW Max Min Mean Stdev Paddock SM Max Min Mean Stdev Paddock WH Max Min Mean Stdev
N (%)
P
K
S
Mg
Ca
Fe
Mn
Na
Zn
4.55 2.75 3.85 0.37
5100 3500 4490 383
39000 15000 31390 6472
3900 2500 2998 244
2900 1860 2356 238
2900 1540 2042 270
720 169 348 139
890 330 614 119
13500 2300 4951 2960
26.5 14.2 20.5 2.8
4.96 2.88 4.06 0.38
4600 2600 3463 517
37000 27000 33217 2467
3300 2000 2883 273
2100 1470 1750 141
2500 1560 2051 222
2000 360 809 362
1100 240 540 211
4400 1500 2841 830
38.2 24.3 30.6 2.9
4.78 3.21 4.01 0.36
4200 2800 3402 305
39000 26000 32959 2590
3800 2400 3124 392
2500 1620 2069 202
2700 1500 2052 288
580 191 293 85
590 176 288 77
8600 2700 4780 1282
44.8 20.0 32.2 5.9
4.27 2.52 3.43 0.45
3900 2800 3482 371
34000 27000 32364 2014
2600 2100 2364 150
1890 1540 1695 101
2700 1820 2247 263
340 170 232 46
460 180 348 72
1480 540 927 349
34.4 20.0 28.1 3.8
Table 8. Mean and range of shoot mineral concentrations (mg/kg) in shoots at PI in four paddocks (1998-99 season). Paddock MG Max Min Mean Stdev Paddock RW Max Min Mean Stdev Paddock SM Max Min Mean Stdev Paddock WH Max Min Mean Stdev
N (%)
P
K
S
Mg
Ca
Fe
Mn
Na
Zn
3.15 1.58 2.34 0.42
3500 2500 2995 232
33000 8600 24498 6218
2500 1300 1772 212
3600 1640 2143 449
2900 1340 2049 363
320 98 145 41
480 194 338 75
17500 750 3739 4011
20.2 11.8 15.4 1.7
3.78 1.54 2.39 0.51
3400 1850 2499 396
33000 23000 27870 2207
2400 1230 1736 312
1930 1420 1667 126
3100 1700 2282 281
800 147 290 107
450 154 266 70
3700 340 1169 665
27.8 11.8 19.5 3.4
3.18 1.91 2.56 0.31
3000 1770 2270 214
29000 15500 25031 2675
2300 1360 1702 193
2800 1630 2080 251
3300 1770 2438 372
280 108 174 33
370 156 215 37
7900 690 2492 1392
23.7 13.8 17.6 2.1
2.82 1.72 2.03 0.28
3400 2300 2883 250
30000 22000 26667 1971
1820 1240 1491 136
1620 1330 1476 76
3000 1510 2153 333
320 149 226 45
560 186 383 90
970 340 603 181
23.7 16.3 19.5 2.0
14
Table 9. Mean and range of shoot mineral concentrations (mg/kg) at flowering in paddocks (1998-99 season). Paddock MG Max Min Mean Stdev Paddock RW Max Min Mean Stdev Paddock SM Max Min Mean Stdev Paddock WH Max Min Mean Stdev
N (%)
P
K
S
Mg
Ca
Fe
Mn
Na
Zn
1.37 0.74 1.05 0.16
2100 1430 1721 159
18000 5600 12935 2940
1190 710 944 111
1600 970 1243 136
1750 1000 1283 167
220 68 132 37
440 121 220 78
4800 189 1338 1328
33.6 10.7 15.6 3.7
1.83 0.85 1.21 0.23
2100 1150 1720 208
15900 9200 13230 1431
1320 780 1015 130
1420 880 1115 128
2100 1130 1535 209
440 99 185 79
610 178 330 109
920 92 299 181
29.9 16.1 20.4 2.7
1.49 0.87 1.20 0.15
2100 1480 1813 128
17100 11800 14633 1200
1200 760 962 98
1460 970 1209 109
1850 1110 1418 177
182 69 117 27
250 84 158 40
1650 86 441 358
26.3 14.0 18.8 2.0
1.08 0.75 0.91 0.10
1730 1080 1495 173
15500 9800 12770 1553
910 600 720 81
1220 880 1006 79
1730 1040 1442 168
340 121 204 60
640 220 418 120
460 94 228 78
20.2 12.9 15.6 2.0
Figure 5. Mean and range of grain mineral concentrations in 4 different paddocks (macro elements).
Mean grain mineral concentration in 4 paddocks (macro elements) 3500
Concentration (mg/kg) exc N (g/kg)
N P K S Mg
3000
2500
2000
1500
1000
500
0
Paddock MG
Paddock RW
Paddock SM
15
Paddock WH
Figure 6. Mean and range of grain mineral concentrations in 4 different paddocks (micro elements).
Mean grain mineral concentration in 4 paddocks (micro elements) Concentration (mg/kg)
60
50
Fe
40
30
Mn
20
Na
10
Zn
0
Paddock MG
Paddock W
Paddock SM
Paddock WH
Mineral
Figure 7. Mean and range of shoot mineral concentrations in 4 different paddocks (macro elements).
Mineral concentration (mg/kg)
Mean shoot mineral concentration in 4 paddocks (macro elements) 4000 3500 3000 2500 2000 1500 1000 500 0 Paddock SM
Paddock RW
Paddock MG
16
Paddock WH
N P K S Mg Ca Na
Figure 8. Mean and range of shoot mineral concentrations in 4 different paddocks (micro elements).
Mean shoot mineral concentration in 4 paddocks (micro elements) Fe Mn B Zn
Mineral concentration (mg/kg)
500 450 400 350 300 250 200 150 100 50 0 Paddock SM
Paddock RW
Paddock MG
17
Paddock WH
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.
6216100.00
6216000.00
6215900.00
422600.00
422700.00
422800.00
422900.00
423000.00
423100.00
Figure 9b: A minimum curvature surface created using 50 soil P measurements for the above paddock.
6216100.00
6216000.00
6215900.00
422600.00
422700.00
422800.00
422900.00
423000.00
423100.00
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. 18
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.
19
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 INPUTS SEED FERTILISER IRRIGATION WATER Total Inputs
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EXPORT GRAIN STUBBLE BURNT N losses BALANCE STUBBLE INCORPORATED STUBBLE BURNT *insufficient data
N
S
P
K
Mg
Ca
Cu
Fe
Mn
Na
Zn
1.50 120 4.6
0.12 3.5 18
0.37 4.6 0.67
0.47 0 3.86
0.16 0 15.3
0.04 3.6 24.5
0.001 0 *
0.004 0 *
0.014 0 *
0.011 0 42.4
0.003 0.015 *
126.1
21.6
5.6
4.3
15.5
28.1
0.001
0.004
0.014
42.4
0.018
93 57 42
7.7 5.4
23.1 2.4
29.1 97
9.7 8
2.2 13.5
0.033 *
0.233 *
0.891 *
0.664 *
0.166 *
-9.0
13.9
-17.4
-24.7
5.7
25.9
-0.033
-0.229
-0.877
41.7
-0.149
-66.0
8.5
-19.8
-121.7
-2.3
12.4
*
*
*
*
*
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.
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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. 22
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.
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