Three putative types of El Nino revealed by spatial variability in impact on Australian wheat yield

The El Nino-Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Nino events are often associated with severe drought conditions. However, El Nino events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Nino are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (footprints) found in the El Nino impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean-atmosphere dynamics identifies three types of El Nino differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Nino events on agricultural systems.

Statistical forecasting of the Australian macadamia crop

Three types of forecasts of the total Australian production of macadamia nuts (t nut-in-shell) have been produced early each year since 2001. The first is a long-term forecast, based on the expected production from the tree census data held by the Australian Macadamia Society, suitably scaled up for missing data and assumed new plantings each year. These long-term forecasts range out to 10 years in the future, and form a basis for industry and market planning. Secondly, a statistical adjustment (termed the climate-adjusted forecast) is made annually for the coming crop. As the name suggests, climatic influences are the dominant factors in this adjustment process, however, other terms such as bienniality of bearing, prices and orchard aging are also incorporated. Thirdly, industry personnel are surveyed early each year, with their estimates integrated into a growers and pest-scouts forecast. Initially conducted on a 'whole-country' basis, these models are now constructed separately for the six main production regions of Australia, with these being combined for national totals. Ensembles or suites of step-forward regression models using biologically-relevant variables have been the major statistical method adopted, however, developing methodologies such as nearest-neighbour techniques, general additive models and random forests are continually being evaluated in parallel. The overall error rates average 14% for the climate forecasts, and 12% for the growers' forecasts. These compare with 7.8% for USDA almond forecasts (based on extensive early-crop sampling) and 6.8% for coconut forecasts in Sri Lanka. However, our somewhatdisappointing results were mainly due to a series of poor crops attributed to human reasons, which have now been factored into the models. Notably, the 2012 and 2013 forecasts averaged 7.8 and 4.9% errors, respectively. Future models should also show continuing improvement, as more data-years become available.

Validation of the Deardorff model for estimating energy balance components for a sugarcane crop

The quantification of the available energy in the environment is important because it determines photosynthesis, evapotranspiration and, therefore, the final yield of crops. Instruments for measuring the energy balance are costly and indirect estimation alternatives are desirable. This study assessed the Deardorff's model performance during a cycle of a sugarcane crop in Piracicaba, State of São Paulo, Brazil, in comparison to the aerodynamic method. This mechanistic model simulates the energy fluxes (sensible, latent heat and net radiation) at three levels (atmosphere, canopy and soil) using only air temperature, relative humidity and wind speed measured at a reference level above the canopy, crop leaf area index, and some pre-calibrated parameters (canopy albedo, soil emissivity, atmospheric transmissivity and hydrological characteristics of the soil). The analysis was made for different time scales, insolation conditions and seasons (spring, summer and autumn). Analyzing all data of 15 minute intervals, the model presented good performance for net radiation simulation in different insolations and seasons. The latent heat flux in the atmosphere and the sensible heat flux in the atmosphere did not present differences in comparison to data from the aerodynamic method during the autumn. The sensible heat flux in the soil was poorly simulated by the model due to the poor performance of the soil water balance method. The Deardorff's model improved in general the flux simulations in comparison to the aerodynamic method when more insolation was available in the environment.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

A hybrid commodity price-forecasting model applied to the sugar-alcohol sector

Accurate price forecasting for agricultural commodities can have significant decision-making implications for suppliers, especially those of biofuels, where the agriculture and energy sectors intersect. Environmental pressures and high oil prices affect demand for biofuels and have reignited the discussion about effects on food prices. Suppliers in the sugar-alcohol sector need to decide the ideal proportion of ethanol and sugar to optimise their financial strategy. Prices can be affected by exogenous factors, such as exchange rates and interest rates, as well as non-observable variables like the convenience yield, which is related to supply shortages. The literature generally uses two approaches: artificial neural networks (ANNs), which are recognised as being in the forefront of exogenous-variable analysis, and stochastic models such as the Kalman filter, which is able to account for non-observable variables. This article proposes a hybrid model for forecasting the prices of agricultural commodities that is built upon both approaches and is applied to forecast the price of sugar. The Kalman filter considers the structure of the stochastic process that describes the evolution of prices. Neural networks allow variables that can impact asset prices in an indirect, nonlinear way, what cannot be incorporated easily into traditional econometric models.

The impact of El Nino Southern Oscillation phases on off-season maize yield for a subtropical region of Brazil

In recent years, maize has become one of the main alternative crops for the autumn winter growing season in the central-western and southeastern regions of Brazil. However, water deficits, sub-optimal temperatures and low solar radiation levels are common problems that are experienced during this growing season by local farmers. One methodology to assess the impact of variable weather conditions on crop production is the use of crop simulation models. The goal of this study was to evaluate the effect of climate variability on maize yield for a subtropical region of Brazil. Specific objectives for this study were (1) to analyse the effect of El Nino Southern Oscillation (ENSO) on precipitation and air temperature for four locations in the state of Sao Paulo and (2) to analyse the impact of ENSO on maize grown off-season for the same four locations using a crop simulation model. For each site, historical weather data were categorised as belonging to one of three phases of ENSO: El Nino (warm sea surface temperature anomalies in the Pacific), La Nina (cool sea surface temperature anomalies) or neutral, based on an index derived from observed sea surface temperature anomalies. During El Nino, there is a tendency for an increase in the rainfall amount during May for the four selected locations, and also during April, mainly in three of the locations, resulting in an increase in simulated maize yield planted between February 15 and March 15. In general, there was a decrease in the simulated yield for maize grown off-season during neutral years. This study showed how a crop model can be used to assess the impact of climate variability on the yield of maize grown off-season in a subtropical region of Brazil. The outcomes of this study can be very useful for both policy makers and local farmers for agricultural planning and decision making. Copyright (C) 2009 Royal Meteorological Society

Yield loss caused by huanglongbing in different sweet orange cultivars in So Paulo, Brazil

Citrus huanglongbing (HLB) reduces an affected orchard`s economic life. This work aimed to characterize yield loss due to HLB for different sweet orange cultivars and determine the relationship between disease severity and yield. Disease severity and yield were assessed on 949 individual trees distributed in 11 different blocks from sweet orange cultivars Hamlin, Westin, Pera and Valencia. In each block, plants showing a range of HLB severity levels and asymptomatic plants were selected. Total yield (weight of harvested fruit), mean weight of asymptomatic and symptomatic fruit, relative yield (symptomatic tree yield/mean yield of asymptomatic trees from the same block) and relative number of fruits (fruit number from symptomatic tree/mean number of fruits from asymptomatic trees from the same block) were determined. The weight of symptomatic fruit was lower than the weight of asymptomatic fruit, but the weights of asymptomatic and symptomatic fruit were not correlated with disease severity, indicating that the effects of HLB were restricted to symptomatic branches. The relationship of the relative yield with HLB severity can be satisfactorily described by a negative exponential model. The rates of yield decrease as a function of disease severity were similar for all assessed cultivars. A relative yield (up to 19%) was observed even for trees where disease severity was 100%. The strong linear relationship between relative number of fruits per tree and the relative yield per tree suggested that the yield reduction was due primarily to early fruit drop or lack of fruit set on affected branches.

Analysis of genomic and functional RFLP derived markers associated with sucrose content, fiber and yield QTLs in a sugarcane (Saccharum spp.) commercial cross

Expressed sequence tags derived markers have a great potential to be used in functional map construction and QTL tagging. In the present work, sugarcane genomic probes and expressed sequence tags having homology to genes, mostly involved in carbohydrate metabolism were used in RFLP assays to identify putative QTLs as well as their epistatic interactions for fiber content, cane yield, pol and tones of sugar per hectare, at two crop cycles in a progeny derived from a bi-parental cross of sugarcane elite materials. A hundred and twenty marker trait associations were found, of which 26 at both crop cycle and 32 only at first ratoon cane. A sucrose synthase derived marker was associated with a putative QTL having a high negative effect on cane yield and also with a QTL having a positive effect on Pol at both crop cycles. Fifty digenic epistatic marker interactions were identified for the four traits evaluated. Of these, only two were observed at both crop cycles.

Water use and crop coefficient for hybrid DKB 390

Water use and crop coefficient for hybrid DKB 390. This work aims to characterize the water use of maize hybrid DKB 390 under suitable conditions of irrigation for both sufficient and below-optimal situations of nitrogen supply. Crop coefficient values for different stages are also presented as a result, in order to provide the basis for crop water budget and management throughout the cycle. A field experiment was carried Out during the main season, in which biomass, soil moisture, leaf area, climate data and light transmittance were evaluated. These have allowed deriving water balance, use and efficiency. The mentioned genotype requires around 600 nun for high yield targets, being less efficient when led under below-optimal nitrogen fertilization.

Does maintaining green leaf area in sorghum improve yield under drought? I. Leaf growth and senescence

Production of sorghum [Sorghum bicolor (L.) Moench], an important cereal crop in semiarid regions of the world, is often limited by drought. When water is limiting during the grain-filling period, hybrids possessing the stay-green trait maintain more photosynthetically active leaves than hybrids not possessing this trait. To improve yield under drought, knowledge of the extent of genetic variation in green leaf area retention is required. Field studies were undertaken in north-eastern Australia on a cracking and self-mulching gray clay to determine the effects of water regime and hybrid on the components of green leaf area at maturity (GLAM). Nine hybrids varying in stay-green were grown under a fully irrigated control, postflowering water deficit, and terminal (pre- and postflowering) water deficit. Water deficit reduced GLAM by 67% in the terminal drought treatment compared with the fully irrigated control. Under terminal water deficit, hybrids possessing the B35 and KS19 sources of stay-green retained more GLAM (1260 cm(2) plant(-1)) compared with intermediate (780 cm(2) plant(-1)) and senescent (670 cm(2) plant(-1)) hybrids. RQL12 hybrids (KS19 source of stay-green) displayed delayed onset and reduced rate of senescence; A35 hybrids displayed only delayed onset. Visual rating of green leaf retention was highly correlated with measured GLAM, although this procedure is constrained by an inability to distinguish among the functional mechanisms determining the phenotype. Linking functional rather than phenotypic differences to molecular markers may improve the efficiency of selecting for traits such as stay-green.

Environmental control of potential yield of sunflower in the subtropics

A simple framework was used to analyse the determinants of potential yield of sunflower (Helianthus annuus L.) in a subtropical environment. The aim was to investigate the stability of the determinants crop duration, canopy light interception, radiation use efficiency (RUE), and harvest index (HI) at 2 sowing times and with 3 genotypes differing in crop maturity and stature. Crop growth, phenology, light interception, yield, prevailing temperature, and radiation were recorded and measured throughout the crop cycle. Significant differences in grain yield were found between the 2 sowings, but not among genotypes within each sowing. Mean yields (0% moisture) were 6 . 02 and 2 . 17 t/ha for the first sowing, on 13 September (S1), and the second sowing, on 5 March (S2), respectively. Exceptionally high yields in S1 were due to high biomass assimilation associated with the high radiation environment, high light interception owing to a greater leaf area index, and high RUE (1 . 47-1 . 62 g/MJ) across genotypes. It is proposed that the high RUE was caused by high levels of available nitrogen maintained during crop growth by frequent applications of fertiliser and sewage effluent as irrigation. In addition to differences in the radiation environment, the assimilate partitioned to grain was reduced in S2 associated with a reduction in the duration of grain-filling. Harvest index was 0 . 40 in S1 and 0 . 25 in S2. It is hypothesised that low minimum temperatures experienced in S2 reduced assimilate production and partitioning, causing premature maturation.

White stem borer damage and grain yield in irrigated rice in West Java, Indonesia

Factors influencing the relationship between whiteheads caused by the white stem borer Scirpophaga innotata (Walker) and grain yield were investigated. We determined the effect of different numbers of whiteheads on grain yield using different cultivars, nitrogen application, and at different field locations in Cilamaya, West Java. At the same number of panicles and whiteheads per plant, yield reduction is greater in cisadane than in IR64. With increasing nitrogen application, the range in panicle height increased. Except for Ketan, more whiteheads were recorded in shorter panicles. Two locations planted to the same cultivar showed different relationships between whiteheads and grain yield. The relationship between whiteheads and grain yield depends on the distribution of whiteheads in the field. Unless these factors have been taken into consideration, it may be difficult to make a damage prediction of white stem borer in the field. (C) 1997 Published by Elsevier Science Ltd.

Establishment of Nilaparvata Iugens Stl in rice crop nurseries: a possible source of outbreaks

We tested the hypothesis that early-planted seedbeds of rioe are mere heavily infested with brown planthopper (BPH) than later seedbeds, and that transplanted plants with lBPH are a source of subsequent population increase and possible outbreaks. The experiments were conducted at CARDI and Takeo province in wet season 2000 and early wet 2 season 200 I. BPH at O. 25. 50, 100, 200 1m were infested onto plants with low and high fertilizer treatments. Rice seeds of varieties moderately and highly susceptible to BPH were sown 3 weeks early, 2 weeks early, at the normal time, and later than normal (5 weeks) and treated with low and high fertilizer rates. At Takeo, the 3< weeks early seedbeds were infested by BPH migration, and both varieties with high fertilizer caught more immigrant insects and subsequently had damaging outbreaks of BPH in the third generation. At CARDl, no seedbeds were infested with immigrant BPH. Seedbeds in areas with continuous cropping of rice have a high risk of BPH attack, Seedlings infested with 200, 100, and 50 BPI[/m2 resulted in death of the plant. Plants with 100 and 200 BPH/m'! were kj[Jed sooner. With 25 BPIVm2 plants were not kllled, but subsequent population increase caused yi eld reduction. Yield loss was high ill higlh fertilizer treated plants. Key words , ,

Factors affecting cane yield and commercial cane sugar in the Tully district

The Tully Sugar Mill has collected information about sugarcane supplied for crushing from every block in the mill district from 1970 to 1999. Data from 1988 to 1999 were analysed to understand the extent of the variation in cane yield per hectare and commercial cane sugar in the Tully mill area. The key factors influencing the variation in cane yield and commercial cane sugar in this commercial environment were identified and the variance components computed using a restricted maximum likelihood methodology. Cane yield was predominantly influenced by the year in which it was harvested, the month when the crop was ratooned (month of harvest in the previous year) and the farm of origin. These variables were relatively more important than variety, age of crop or crop class (plant crop, first ratoon through to fourth or older ratoons) and fallowing practice (fallow or ploughout-replant). The month-of-ratooning effect was relatively stable from year-to-year. Commercial cane sugar was influenced by the year of harvest, the month of harvest and their interaction, in that the influence of the month of harvest varied from year to year. Variety and farm differences were also significant but accounted for a much lower portion of the variation in commercial cane sugar. An empirical model was constructed from the key factors that influenced commercial cane sugar and cane yield to quantify their combined influence on sugar yield (t/ha). This may be used to assist mill personnel to predict their activities more accurately, for example to calculate the impact of a late finish to the current harvest season on the following year's crop.