Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

Nutrient uptake in tropical turfgrasses growing in winter in southern Queensland

The effects of fertilisers on 8 tropical turfgrasses growing in 100-L bags of sand were studied over winter in Murrumba Downs, just north of Brisbane in southern Queensland (latitude 27.4°S, longitude 153.1°E). The species used were: Axonopus compressus (broad-leaf carpetgrass), Cynodon dactylon (bermudagrass 'Winter Green') and C. dactylon x C. transvaalensis hybrid ('Tifgreen'), Digitaria didactyla (Queensland blue couch), Paspalum notatum (bahiagrass '38824'), Stenotaphrum secundatum (buffalograss 'Palmetto'), Eremochloa ophiuroides (centipedegrass 'Centec') and Zoysia japonica (zoysiagrass 'ZT-11'). Control plots were fertilised with complete fertilisers every month from May to September (72 kg N/ha, 31 kg P/ha, 84 kg K/ha, 48 kg S/ha, 30 kg Ca/ha and 7.2 kg Mg/ha), and unfertilised plots received no fertiliser. Carpetgrass and standard bermudagrass were the most sensitive species to nutrient supply, with lower shoot dry weights in the unfertilised plots (shoots mowed to thatch level) compared with the fertilised plots in June. There were lower shoot dry weights in the unfertilised plots in July for all species, except for buffalograss, centipedegrass and zoysiagrass, and lower shoot dry weights in the unfertilised plots in August for all species, except for centipedegrass. At the end of the experiment in September, unfertilised plots were 11% of the shoot dry weights of fertilised plots, with all species affected. Mean shoot nitrogen concentrations fell from 3.2 to 1.7% in the unfertilised plots from May to August, below the sufficiency range for turfgrasses (2.8-3.5%). There were also declines in P (0.45-0.36%), K (2.4-1.5%), S (0.35-0.25%), Mg (0.24-0.18%) and B (9-6 mg/kg), which were all in the sufficiency range. The shoots in the control plots took up the following levels (kg/ha.month) of nutrients: N, 10.0-27.0; P, 1.6-4.0; K, 8.2-19.8; S, 1.0-4.2; Ca, 1.1-3.3; and Mg, 0.8-2.2, compared with applications (kg/ha.month) of: N, 72; P, 31; K, 84; S, 48; Ca, 30; and Mg, 7.2, indicating a recovery of 14-38% for N, 5-13% for P, 10-24% for K, 2-9% for S, 4-11% for Ca and 11-30% for Mg. These results suggest that buffalograss, centipedegrass and zoysiagrass are less sensitive to low nutrient supply than carpetgrass, bermudagrass, blue couch and bahiagrass. Data on nutrient uptake showed that the less sensitive species required only half or less of the nitrogen required to maintain the growth of the other grasses, indicating potential savings for turf managers in fertiliser costs and the environment in terms of nutrients entering waterways.

Cynodon dactylon, Green Couch Grass, Bermuda Grass 'Winter Gem'

‘Winter Gem’ is a selection from a cross between ‘Wintergreen’ and Couch 5 (also designated C5). Couch 5 was a selection from an earlier series of crosses by the breeder between ‘Wintergreen’ and a number of Cynodon dactylon accessions, which were collected by the breeder from the Mornington Peninsula area of Victoria between 1986 and 1990. C5 was an experimental breeding line, and was not subsequently reserved as vegetative germplasm. Living material of C5 is no longer in existence. Following the crossing of Couch 5 and ‘Wintergreen’ in 1998, the resultant seed was germinated on moist blotting paper. Individual seedlings, a total of 150 in number, were planted into 150mm pots and these plants observed during 1998 and 1999. During the summer of 1999-2000, the majority of the seedling plants were culled on the basis of their shoot density, leaf texture, internode length, and colour. In the spring of 2000, the remaining 20 potted seedlings were planted individually into 4m2 plots at the Evergreen Turf farm at Pakenham (Victoria), and allowed to expand fully across these plots. The final selection of Seedling 9 (later designated DN9) in late 2002 was based on shoot density, leaf texture, and retention of winter colour as expressed in these plots. Propagation: The original plant had been multiplied through four (4) vegetative expansions prior to PBR application without showing any discernible off types. Breeder: David Nickson, Frankston, VIC. PBR Certificate Number 3132, Application Number 2005/290, granted 11 September 2006.

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Improved cultivar.

The role of tillage, fertiliser and forage species in sustaining dairying based on crops in southern Queensland 1. Winter-dominant forage systems.

Field studies were conducted over 5 years on two dairy farms in southern Queensland to evaluate the impacts of zero-tillage, nitrogen (N) fertiliser and legumes on a winter-dominant forage system based on raingrown oats. Oats was able to be successfully established using zero-tillage methods, with no yield penalties and potential benefits in stubble retention over the summer fallow. N fertiliser, applied at above industry-standard rates (140 vs. 55 kg/ha.crop) in the first 3 years, increased forage N concentration significantly and had residual effects on soil nitrate-N at both sites. At one site, crop yield was increased by 10 kg DM/ha. kg fertiliser N applied above industry-standard rates. The difference between sites in fertiliser response reflected contrasting soil and fertiliser history. There was no evidence that modifications to oats cropping practices (zero-tillage and increased N fertiliser) increased surface soil organic carbon (0-10 cm) in the time frame of the present study. When oats was substituted with annual legumes, there were benefits in improved forage N content of the oat crop immediately following, but legume yield was significantly inferior to oats. In contrast, the perennial legume Medicago sativa was competitive in biomass production and forage quality with oats at both sites and increased soil nitrate-N levels following termination. However, its contribution to winter forage was low at 10% of total production, compared with 40% for oats, and soil water reserves were significantly reduced at one site, which had an impact on the following oat production. The study demonstrated that productive grazed oat crops can be grown using zero tillage and that increased N fertiliser is more consistent in its effect on N concentration than on forage yield. A lucerne ley provides a strategy for raising soil nitrate-N concentration and increasing overall forage productivity, although winter forage production is reduced.

Estimating winter crop area across seasons and regions using time-sequential MODIS imagery.

The wheat grain industry is Australia's second largest agricultural export commodity. There is an increasing demand for accurate, objective and near real-time crop production information by industry. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to capture reflectance over large areas at acceptable pixel scale, cost and accuracy. The use of multi-temporal MODIS-enhanced vegetation index (EVI) imagery to determine crop area was investigated in this article. Here the rigour of the harmonic analysis of time-series (HANTS) and early-season metric approaches was assessed when extrapolating over the entire Queensland (QLD) cropping region for the 2005 and 2006 seasons. Early-season crop area estimates, at least 4 months before harvest, produced high accuracy at pixel and regional scales with percent errors of -8.6% and -26% for the 2005 and 2006 seasons, respectively. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. The errors for specific area estimates for wheat, barley and chickpea were 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006), respectively. Area estimates of total winter crop, wheat, barley and chickpea resulted in coefficient of determination (R(2)) values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high coefficient of determination (0.87) was achieved for total winter crop area estimates in August across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps. The extrapolability of these approaches to determine total and specific winter crop area estimates, well before flowering, showed good utility across larger areas and seasons. Hence, it is envisaged that this technology might be transferable to different regions across Australia.

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

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

Soil phosphorus–crop response calibration relationships and criteria for winter cereal crops grown in Australia

Soil testing is the most widely used tool to predict the need for fertiliser phosphorus (P) application to crops. This study examined factors affecting critical soil P concentrations and confidence intervals for wheat and barley grown in Australian soils by interrogating validated data from 1777 wheat and 150 barley field treatment series now held in the BFDC National Database. To narrow confidence intervals associated with estimated critical P concentrations, filters for yield, crop stress, or low pH were applied. Once treatment series with low yield (<1 t/ha), severe crop stress, or pHCaCl2 <4.3 were screened out, critical concentrations were relatively insensitive to wheat yield (>1 t/ha). There was a clear increase in critical P concentration from early trials when full tillage was common compared with those conducted in 1995–2011, which corresponds to a period of rapid shift towards adoption of minimum tillage. For wheat, critical Colwell-P concentrations associated with 90 or 95% of maximum yield varied among Australian Soil Classification (ASC) Orders and Sub-orders: Calcarosol, Chromosol, Kandosol, Sodosol, Tenosol and Vertosol. Soil type, based on ASC Orders and Sub-orders, produced critical Colwell-P concentrations at 90% of maximum relative yield from 15 mg/kg (Grey Vertosol) to 47 mg/kg (Supracalcic Calcarosols), with other soils having values in the range 19–27 mg/kg. Distinctive differences in critical P concentrations were evident among Sub-orders of Calcarosols, Chromosols, Sodosols, Tenosols, and Vertosols, possibly due to differences in soil properties related to P sorption. However, insufficient data were available to develop a relationship between P buffering index (PBI) and critical P concentration. In general, there was no evidence that critical concentrations for barley would be different from those for wheat on the same soils. Significant knowledge gaps to fill to improve the relevance and reliability of soil P testing for winter cereals were: lack of data for oats; the paucity of treatment series reflecting current cropping practices, especially minimum tillage; and inadequate metadata on soil texture, pH, growing season rainfall, gravel content, and PBI. The critical concentrations determined illustrate the importance of recent experimental data and of soil type, but also provide examples of interrogation pathways into the BFDC National Database to extract locally relevant critical P concentrations for guiding P fertiliser decision-making in wheat and barley.

Variation in the hindgut microbial communities of the Florida manatee, Trichechus manatus latirostris over winter in Crystal River, Florida

The Florida manatee, Trichechus manatus latirostris, is a hindgut-fermenting herbivore. In winter, manatees migrate to warm water overwintering sites where they undergo dietary shifts and may suffer from cold-induced stress. Given these seasonally induced changes in diet, the present study aimed to examine variation in the hindgut bacterial communities of wild manatees overwintering at Crystal River, west Florida. Faeces were sampled from 36 manatees of known sex and body size in early winter when manatees were newly arrived and then in mid-winter and late winter when diet had probably changed and environmental stress may have increased. Concentrations of faecal cortisol metabolite, an indicator of a stress response, were measured by enzyme immunoassay. Using 454-pyrosequencing, 2027 bacterial operational taxonomic units were identified in manatee faeces following amplicon pyrosequencing of the 16S rRNA gene V3/V4 region. Classified sequences were assigned to eight previously described bacterial phyla; only 0.36% of sequences could not be classified to phylum level. Five core phyla were identified in all samples. The majority (96.8%) of sequences were classified as Firmicutes (77.3 ± 11.1% of total sequences) or Bacteroidetes (19.5 ± 10.6%). Alpha-diversity measures trended towards higher diversity of hindgut microbiota in manatees in mid-winter compared to early and late winter. Beta-diversity measures, analysed through permanova, also indicated significant differences in bacterial communities based on the season.

Wheat biomass and yield increased when populations of the root-lesion nematode (Pratylenchus thornei) were reduced through sequential rotation of partially resistant winter and summer crops

The root-lesion nematode, Pratylenchus thornei, can reduce wheat yields by >50%. Although this nematode has a broad host range, crop rotation can be an effective tool for its management if the host status of crops and cultivars is known. The summer crops grown in the northern grain region of Australia are poorly characterised for their resistance to P. thornei and their role in crop sequencing to improve wheat yields. In a 4-year field experiment, we prepared plots with high or low populations of P. thornei by growing susceptible wheat or partially resistant canaryseed (Phalaris canariensis); after an 11-month, weed-free fallow, several cultivars of eight summer crops were grown. Following another 15-month, weed-free fallow, P. thornei-intolerant wheat cv. Strzelecki was grown. Populations of P. thornei were determined to 150 cm soil depth throughout the experiment. When two partially resistant crops were grown in succession, e.g. canaryseed followed by panicum (Setaria italica), P. thornei populations were <739/kg soil and subsequent wheat yields were 3245 kg/ha. In contrast, after two susceptible crops, e.g. wheat followed by soybean, P. thornei populations were 10 850/kg soil and subsequent wheat yields were just 1383 kg/ha. Regression analysis showed a linear, negative response of wheat biomass and grain yield with increasing P. thornei populations and a predicted loss of 77% for biomass and 62% for grain yield. The best predictor of wheat yield loss was P. thornei populations at 0-90 cm soil depth. Crop rotation can be used to reduce P. thornei populations and increase wheat yield, with greatest gains being made following two partially resistant crops grown sequentially.