Characterization of maize growing environments in eastern and southern Africa using the APSIM model

Maize grown in eastern and southern Africa experiences random occurrences of drought. This uncertainty creates difficulty in developing superior varieties and their agronomy. Characterisation of drought types and their frequencies could help in better defining selection environments for improving resistance to drought. We used the well tested APSIM maize model to characterise major drought stress patterns and their frequencies across six countries of the region including Ethiopia, Kenya, Tanzania, Malawi, Mozambique and Zimbabwe. The database thus generated covered 35 sites, 17 to 86 years of daily climate records, 3 varieties and 3 planting densities from a total of 11,174 simulations. The analysis identified four major drought environment types including those characterised by low-stress which occurred in 42% of the years, mid-season drought occurring in 15% of the years, late-terminal stress which occurred in 22% of the years and early-terminal drought occurring in 21% of the years. These frequencies varied in relation to sites, genotypes and management. The simulations showed that early terminal stress could result in a yield reduction of 70% compared with low-stress environmental types. The study presents the importance of environmental characterization in contributing to maize improvement in eastern and southern Africa.

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.

Maize yield determination in the Northern Region: Hybrid by environment by management interactions

In Maize, as with most cereals, grain yield is mostly determined by the total grain number per unit area, which is highly related to the rate of crop growth during the critical period around silking. Management practices such as plant density or nitrogen fertilization can affect the growth of the crop during this period, and consequently the final grain yield. Across the Northern Region maize is grown under a large range of plant populations under high year-to-year rainfall variability. Clear guidelines on how to match hybrids and management across environments and expected seasonal condition, would allow growers to increase yields and profits while managing risks. The objective of this research was to screen the response of commercial maize hybrids differing in maturity and prolificity (i.e. multi or single cobbing) types for their efficiency in the allocation of biomass into grain.

Impact of compost, vermicompost and biochar on soil fertility, maize yield and soil erosion in Northern Vietnam: A three year mesocosm experiment

Compost, vermicompost and biochar amendments are thought to improve soil quality and plant yield. However, little is known about their long-term impact on crop yield and the environment in tropical agro-ecosystems. In this study we investigated the effect of organic amendments (buffalo manure, compost and verrnicompost) and biochar (applied alone or with vermicompost) on plant yield, soil fertility, soil erosion and water dynamics in a degraded Acrisol in Vietnam. Maize growth and yield, as well as weed growth, were examined for three years in terrestrial mesocosms under natural rainfall. Maize yield and growth showed high inter-annual variability depending on the organic amendment. Vermicompost improved maize growth and yield but its effect was rather small and was only significant when water availability was limited (year 2). This suggests that vermicompost could be a promising substrate for improving the resistance of agrosystems to water stress. When the vermicompost biochar mixture was applied, further growth and yield improvements were recorded in some cases. When applied alone, biochar had a positive influence on maize yield and growth, thus confirming its interest for improving long-term soil productivity. All organic amendments reduced water runoff, soil detachment and NH4+ and NO3- transfer to water. These effects were more significant with vermicompost than with buffalo manure and compost, highlighting that the beneficial influence of vermicompost is not limited to its influence on plant yield. In addition, this study showed for the first time that the combination of vermicompost and biochar may not only improve plant productivity but also reduce the negative impact of agriculture on water quality. (C) 2015 Elsevier B.V. All rights reserved.