Ultraviolet radiation effects on fruit surface respiration and chlorophyll fluorescence

High-value fruit crops are exposed to a range of environmental conditions that can reduce fruit quality. Solar injury (SI) or sunburn is a common disorder in tropical, sub-tropical, and temperate climates and is related to: 1) high fruit surface temperature; 2) high visible light intensity; and, 3) ultraviolet radiation (UV). Positional changes in fruit that are caused by increased weight or abrupt changes that result from summer pruning, limb breakage, or other damage to the canopy can expose fruit to high solar radiation levels, increased fruit surface temperatures, and increased UV exposure that are higher than the conditions to which they are adapted. In our studies, we examined the effects of high fruit surface temperature, saturating photosynthetically-active radiation (PAR), and short-term UV exposure on chlorophyll fluorescence, respiration, and photosynthesis of fruit peel tissues from tropical and temperate fruit in a simulation of these acute environmental changes. All tropical fruits (citrus, macadamia, avocado, pineapple, and custard apple) and the apple cultivars 'Gala', 'Gold Rush', and 'Granny Smith' increased dark respiration (A0) when exposed to UV, suggesting that UV repair mechanisms were induced. The maximum quantum efficiency of photosystem II (Fv/Fm) and the quantum efficiency of photosystem II (ΦII) were unaffected, indicating no adverse effects on photosystem II (PSII). In contrast, 'Braeburn' apple had a reduced Fv/Fm with no increase in A0 on all sampling dates. There was a consistent pattern in all studies. When Fv/Fm was unaffected by UV treatment, A0 increased significantly. Conversely, when Fv/Fm was reduced by UV treatment, then A0 was unaffected. The pattern suggests that when UV repair mechanisms are effective, PSII is adequately protected, and that this protection occurs at the cost of higher respiration. However, when the UV repair mechanisms are ineffective, not only is PSII damaged, but there is additional short-term damage to the repair mechanisms, indicated by a lack of respiration to provide energy.

Using APSIM-soiltemp to simulate soil temperature in the podding zone of peanut

Measurement or accurate simulation of soil temperature is important for improved understanding and management of peanuts (Arachis hypogaea L.), due to their geocarpic habit. A module of the Agricultural Production Systems Simulator Model (APSIM), APSIM-soiltemp, which uses input of ambient temperature, rainfall and solar radiation in conjunction with other APSIM modules, was evaluated for its ability to simulate surface 5 cm soil temperature in 35 peanut on-farm trials conducted between 2001 and 2005 in the Burnett region (25°36'S to 26°41'S, 151°39'E to 151°53'E). Soil temperature simulated by the APSIM-soiltemp module, from 30 days after sowing until maturity, closely matched the measured values (R2 ≥ 0.80)in the first three seasons (2001-04). However, a slightly poorer relationship (R2 = 0.55) between the observed and the simulated temperatures was observed in 2004-05, when the crop was severely water stressed. Nevertheless, over all the four seasons, which were characterised by a range of ambient temperature, leaf area index, radiation and soil water, each of which was found to have significant effects on soil temperature, a close 1:1 relationship (R2 = 0.85) between measured and simulated soil temperatures was observed. Therefore, the pod zone soil temperature simulated by the module can be generally relied on in place of measured input of soil temperature in APSIM applications, such as quantifying climatic risk of aflatoxin accumulation.

Stochastic model for simulating maize yield

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment. © 2012 American Society of Agricultural and Biological Engineers.

Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models

To facilitate marketing and export, the Australian macadamia industry requires accurate crop forecasts. Each year, two levels of crop predictions are produced for this industry. The first is an overall longer-term forecast based on tree census data of growers in the Australian Macadamia Society (AMS). This data set currently accounts for around 70% of total production, and is supplemented by our best estimates of non-AMS orchards. Given these total tree numbers, average yields per tree are needed to complete the long-term forecasts. Yields from regional variety trials were initially used, but were found to be consistently higher than the average yields that growers were obtaining. Hence, a statistical model was developed using growers' historical yields, also taken from the AMS database. This model accounted for the effects of tree age, variety, year, region and tree spacing, and explained 65% of the total variation in the yield per tree data. The second level of crop prediction is an annual climate adjustment of these overall long-term estimates, taking into account the expected effects on production of the previous year's climate. This adjustment is based on relative historical yields, measured as the percentage deviance between expected and actual production. The dominant climatic variables are observed temperature, evaporation, solar radiation and modelled water stress. Initially, a number of alternate statistical models showed good agreement within the historical data, with jack-knife cross-validation R2 values of 96% or better. However, forecasts varied quite widely between these alternate models. Exploratory multivariate analyses and nearest-neighbour methods were used to investigate these differences. For 2001-2003, the overall forecasts were in the right direction (when compared with the long-term expected values), but were over-estimates. In 2004 the forecast was well under the observed production, and in 2005 the revised models produced a forecast within 5.1% of the actual production. Over the first five years of forecasting, the absolute deviance for the climate-adjustment models averaged 10.1%, just outside the targeted objective of 10%.

Rice responses to soil management in a rice-based cropping system in the semi-arid tropics of southern Lombok, Eastern Indonesia

This paper is the first of a series that investigates whether new cropping systems with permanent raised beds (PRBs) or Flat land could be successfully used to increase farmers' incomes from rainfed crops in Lombok in Eastern Indonesia. This paper discusses the rice phase of the cropping system. Low grain yields of dry-seeded rice (Oryza sativa) grown on Flat land on Vertisols in the rainfed region of southern Lombok, Eastern Indonesia, are probably mainly due to (a) erratic rainfall (870-1220 mm/yr), with water often limiting at sensitive growth stages, (b) consistently high temperatures (average maximum - 31 C), and (c) low solar radiation. Farmers are therefore poor, and labour is hard and costly, as all operations are manual. Two replicated field experiments were run at Wakan (annual rainfall = 868 mm) and Kawo (1215 mm) for 3 years (2001/2002 to 2003/2004) on Vertisols in southern Lombok. Dry-seeded rice was grown in 4 treatments with or without manual tillage on (a) PRBs, 1.2 m wide, 200 mm high, separated by furrows 300 mm wide, 200 mill deep, with no rice sown in the well-graded furrows, and (b) well-graded Flat land. Excess surface water was harvested from each treatment and used for irrigation after the vegetative stage of the rice. All operations were manual. There were no differences between treatments in grain yield of rice (mean grain yield = 681 g/m(2)) which could be partly explained by total number of tillers/hill and mean panicle length, but not number of productive tillers/hill, plant height or weight of 1000 grains. When the data from both treatments on PRBs and from both treatments on Flat land, each year at each site were analysed, there were also no differences in grain yield of rice (g/m(2)). When rainfall in the wet season up to harvest was over 1000 mm (Year 2; Wakan, Kawo), or plants were water-stressed during crop establishment (Year 1; Wakan) or during grain-fill (Year 3: Kawo), there were significant differences in grain yield (g/1.5 m(2)) between treatments; generally the grain yield (g/1.5 m(2)) on PRBs with or without tillage was less than that on Flat land with or without tillage. However, when the data from both treatments on PRBs and from both treatments on Flat land, each year at each site, were analysed, the greater grain yield of dry-seeded rice on Flat land (mean yield 1 092 g/1.5 m(2)) than that on PRBs (mean 815 g/1.5 m(2)) was mainly because there were 25% more plants on Flat land. Overall when the data in the 2 outer rows and the 2 inner rows on PRBs were each combined, there was a higher number of productive tillers in the combined outer rows (mean 20.7 tillers/hill) compared with that in the combined inner rows on each PRB (mean 18.2 tillers/hill). However, there were no differences in grain yield between combined rows (mean 142 g/m row). Hence with a gap of 500 mm (the distance between the outer rows of plants on adjacent raised beds), plants did not compensate in grain yield for missing plants in furrows. This suggests that rice (a) also sown in furrows, or (b) sown in 7 rows with narrower row-spacing, or (c) sown in 6 rows with slightly wider row-spacing, and narrower gap between outer rows on adjacent beds, may further increase grain yield (g/1.5 m(2)) in this system of PRBs. The growth and the grain yield (y in g/m(2)) of rainfed rice (with rainfall on-site the only source of water for irrigation) depended mainly on the rainfall (x in mm) in the wet season up to harvest (due either to site or year) with y = 1. 1x -308; r(2) = 0.54; p < 0.005. However, 280 mm (i.e. 32%) of the rainfall was not directly used to produce grain (i.e. when y = 0 g/m(2)). Manual tillage did not affect growth and grain yield of rice (g/m(2); g/1.5 m(2)), either on PRB or on Flat land.

Analysis of Crude Protein and Allergen Abundance in Peanuts (Arachis hypogaea cv. Walter) from Three Growing Regions in Australia

The effects of plant growth conditions on concentrations of proteins, including allergens, in peanut (Arachis hypogaea L.) kernels are largely unknown. Peanuts (cv. Walter) were grown at five sites (Taabinga, Redvale, Childers, Bundaberg, and Kairi) covering three commercial growing regions in Queensland, Australia. Differences in temperature, rainfall, and solar radiation during the growing season were evaluated. Kernel yield varied from 2.3 t/ha (Kairi) to 3.9 t/ha (Childers), probably due to differences in solar radiation. Crude protein appeared to vary only between Kairi and Childers, whereas Ara h 1 and 2 concentrations were similar in all locations. 2D-DIGE revealed significant differences in spot volumes for only two minor protein spots from peanuts grown in the five locations. Western blotting using peanut-allergic serum revealed no qualitative differences in recognition of antigens. It was concluded that peanuts grown in different growing regions in Queensland, Australia, had similar protein compositions and therefore were unlikely to show differences in allergenicity.