Physiological Basis for Genotypic Variation in Tolerance to and Recovery from Pre-flowering Drought in Peanut.

Drought during the pre-flowering stage can increase yield of peanut. There is limited information on genotypic variation for tolerance to and recovery from pre-flowering drought (PFD) and more importantly the physiological traits underlying genotypic variation. The objectives of this study were to determine the effects of moisture stress during the pre-flowering phase on pod yield and to understand some of the physiological responses underlying genotypic variation in response to and recovery from PFD. A glasshouse and field experiments were conducted at Khon Kaen University, Thailand. The glasshouse experiment was a randomized complete block design consisting of two watering regimes, i.e. fully-irrigated control and 1/3 available soil water from emergence to 40 days after emergence followed by adequate water supply, and 12 peanut genotypes. The field experiment was a split-plot design with two watering regimes as main-plots, and 12 peanut genotypes as sub-plots. Measurements of N-2 fixation, leaf area (LA) were made in both experiments. In addition, root growth was measured in the glasshouse experiment. Imposition of PFD followed by recovery resulted in an average increase in yield of 24 % (range from 10 % to 57 %) and 12 % (range from 2 % to 51 %) in the field and glasshouse experiments, respectively. Significant genotypic variation for N-2 fixation, LA and root growth was also observed after recovery. The study revealed that recovery growth following release of PFD had a stronger influence on final yield than tolerance to water deficits during the PFD. A combination of N-2 fixation, LA and root growth accounted for a major portion of the genotypic variation in yield (r = 0.68-0.93) suggesting that these traits could be used as selection criteria for identifying genotypes with rapid recovery from PFD. A combined analysis of glasshouse and field experiments showed that LA and N-2 fixation during the recovery had low genotype x environment interaction indicating potential for using these traits for selecting genotypes in peanut improvement programs.

Characterization of north-eastern Australian environments using APSIM for increasing rainfed maize production

Recurring water stresses are a major risk factor for rainfed maize cropping across the highly diverse agro-ecological environments of Queensland (Qld) and northern New South Wales (NNSW). Enhanced understanding of such agro-ecological diversity is necessary to more consistently sample target production environments for testing and targeting release of improved germplasm, and to improve the efficiency of the maize pre-breeding and breeding programs of Qld and New South Wales. Here, we used the Agricultural Production Systems Simulator (APSIM) – a well validated maize crop model to characterize the key distinctive water stress patterns and risk to production across the main maize growing regions of Qld and NNSW located between 15.8° and 31.5°S, and 144.5° and 151.8°E. APSIM was configured to simulate daily water supply demand ratios (SDRs) around anthesis as an indicator of the degree of water stress, and the final grain yield. Simulations were performed using daily climatic records during the period between 1890 and 2010 for 32 sites-soils in the target production regions. The runs were made assuming adequate nitrogen supply for mid-season maize hybrid Pioneer 3153. Hierarchical complete linkage analyses of the simulated yield resulted in five major clusters showing distinct probability distribution of the expected yields and geographic patterns. The drought stress patterns and their frequencies using SDRs were quantified using multivariate statistical methods. The identified stress patterns included no stress, mid-season (flowering) stress, and three terminal stresses differing in terms of severity. The combined frequency of flowering and terminal stresses was highest (82.9%), mainly in sites-soils combinations in the west of Qld and NNSW. Yield variability across the different sites-soils was significantly related to the variability in frequencies of water stresses. Frequencies of water stresses within each yield cluster tended to be similar, but different across clusters. Sites-soils falling within each yield cluster therefore could be treated as distinct maize production environments for testing and targeting newly developed maize cultivars and hybrids for adaptation to water stress patterns most common to those environments.

The critical role of extreme heat for maize production in the United States

Statistical studies of rainfed maize yields in the United States(1) and elsewhere(2) have indicated two clear features: a strong negative yield response to accumulation of temperatures above 30 degrees C (or extreme degree days (EDD)), and a relatively weak response to seasonal rainfall. Here we show that the process-based Agricultural Production Systems Simulator (APSIM) is able to reproduce both of these relationships in the Midwestern United States and provide insight into underlying mechanisms. The predominant effects of EDD in APSIM are associated with increased vapour pressure deficit, which contributes to water stress in two ways: by increasing demand for soil water to sustain a given rate of carbon assimilation, and by reducing future supply of soil water by raising transpiration rates. APSIM computes daily water stress as the ratio of water supply to demand, and during the critical month of July this ratio is three times more responsive to 2 degrees C warming than to a 20% precipitation reduction. The results suggest a relatively minor role for direct heat stress on reproductive organs at present temperatures in this region. Effects of elevated CO2 on transpiration efficiency should reduce yield sensitivity to EDD in the coming decades, but at most by 25%.

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.

Integrated disease management of stem end rot of mango in the Southern Philippines

This research aimed to develop and evaluate pre- and postharvest management strategies to reduce stem end rot (SER) incidence and extend saleable life of 'Carabao' mango fruits in Southern Philippines. Preharvest management focused on the development and improvement of fungicide spray program, while postharvest management aimed to develop alternative interventions aside from hot water treatment (HWT). Field evaluation of systemic fungicides, namely azoxystrobin ( Amistar 25SC), tebuconazole ( Folicur 25WP), carbendazim ( Goldazim 500SC), difenoconazole ( Score 250SC) and azoxystrobin+difenoconazole ( Amistar Top), reduced blossom blight severity and improved fruit setting and retention, resulting in higher fruit yield but failed to sufficiently suppress SER incidence. Based on these findings, an improved fungicide spray program was developed taking into account the infection process of SER pathogens and fungicide resistance. Timely application of protectant (mancozeb) and systemic fungicides (azoxystrobin, carbendazim and difenoconazole) during the most critical stages of mango flower and fruit development ensured higher harvestable fruit yield and minimally lowered SER incidence. Control of SER was also achieved by employing postharvest treatment such as HWT (52-55°C for 10 min), which significantly prolonged the saleable life of mango fruits. However, extended hot water treatment (EHWT; 46°C pulp temperature for 15 min), rapid heat treatment (RHT; 59°C for 30-60 sec), fungicide dip and promising biological control agents failed to satisfactorily reduce SER and prolong saleable life. In contrast, the integration of the improved spray program as preharvest management practice, and postharvest treatments such as HWT and fungicide dips (azoxystrobin, 150-175 ppm; carbendazim, 312.5 ppm; and tebuconazole, 125-156 ppm), significantly reduced disease and extended marketable life for utmost 8 days.

How weed control and fertilisation influence tree physiological processes and growth at early establishment in an exotic F1 hybrid pine plantation of subtropical Australia

Purpose This study investigated how nitrogen (N) nutrition and key physiological processes varied under changed water and nitrogen competition resulting from different weed control and fertilisation treatments in a 2-year-old F1 hybrid (Pinus elliottii Engelm var. elliottii × P. caribaea var. hondurensis Barr. ex Golf.) plantation on a grey podzolic soil type, in Southeast Queensland. Materials and methods The study integrated a range of measures including growth variables (diameter at ground level (DGL), diameter at breast height (DBH) and height (H)), foliar variables (including foliar N concentration, foliar δ13C and δ15N) and physiological variables (including photosynthesis (An), stomatal conductance (gs), transpiration (E), intrinsic water use efficiency (WUEi) (A/gs) and xylem pressure potential (ΨXPP)) to better understand the mechanisms influencing growth under different weed control and fertilisation treatments. Five levels of weed control were applied: standard (routine), luxury, intermediate, mechanical and nil weed control, all with routine fertilisation plus an additional treatment, routine weed control and luxury fertilisation. Relative weed cover was assessed at 0.8, 1.1 and 1.6 years after plantation establishment to monitor the effectiveness of weed control treatments. Soil investigation included soil ammonium (NH4 +-N), nitrate (NO3 −-N), potentially mineralizable N (PMN), gravimetric soil moisture content (MC), hot water extractable organic carbon (HWETC), hot water extractable total N (HWETN), total C, total N, stable C isotope composition (δ13C), stable N isotope composition (δ15N), total P and extractable K. Results and discussion There were significant relationships between foliar N concentrations and relative weed cover and between tree growth and foliar N concentration or foliar δ15N, but initial site preparation practices also increased soil N transformations in the planting rows reducing the observable effects of weed control on foliar δ15N. A positive relationship between foliar N concentration and foliar δ13C or photosynthesis indicated that increased N availability to trees positively influenced non-stomatal limitations to photosynthesis. However, trees with increased foliar N concentrations and photosynthesis were negatively related to xylem pressure potential in the afternoons which enhanced stomatal limitations to photosynthesis and WUEi. Conclusions Luxury and intermediate weed control and luxury fertilisation positively influenced growth at early establishment by reducing the competition for water and N resources. This influenced fundamental key physiological processes such as the relationships between foliar N concentration, A n, E, gs and ΨXPP. Results also confirmed that time from cultivation is an important factor influencing the effectiveness of using foliar δ15N as an indicator of soil N transformations.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.