Gaseous and leaching nitrogen losses from no-tillage and conventional tillage systems following surface application of cattle manure

Previous studies have demonstrated inconsistent results on the impact of tillage systems on nitrogen (N) losses from field-applied manure. This study assessed the impact of no-tillage (NT) and conventional tillage (CT) systems on gaseous N losses, N₂O:N₂O + N₂ ratios and NO₃⁻-N leaching following surface application of cattle manure. The study was undertaken during the 2003/2004 and 2004/2005 seasons at two field sites in Nova Scotia namely, Streets Ridge (SR) in Cumberland County and the Bio-environmental Engineering Centre (BEEC) in Truro. Results showed that the NT system had higher (p < 0.05) NH₃ losses than CT. Over the two seasons, manure incorporation in CT reduced NH₃ losses on average by 86% at SR and 78% at BEEC relative to NT. At both sites and during both seasons, denitrification rates and N₂O fluxes in NT were generally higher than in CT plots, presumably due to higher soil water and organic matter content in NT. Over the two seasons, mean denitrification rates at SR were 239 and 119 g N ha⁻¹ d⁻¹, while N₂O fluxes were 120 and 64 g N ha⁻¹ d⁻¹ under NT and CT, respectively. At BEEC mean denitrification rates were 114 and 71 g N ha⁻¹ d⁻¹, while N₂O fluxes were 52 and 27 g N ha⁻¹ d⁻¹ under NT and CT, respectively. Conversely, N₂O:N₂O + N₂ ratios were lower in NT than CT suggesting more complete reduction of N₂O to N₂ under NT. When averaged across all soil depths, NO₃⁻-N was higher (p < 0.05) in CT than NT. Nitrate-N decreased with depth at both sites regardless of tillage. In most cases, NO₃⁻-N was higher under CT than NT at all soil depths. Similarly, flow-weighted average NO₃⁻-N concentrations in drainage water were generally higher under CT. This may be partly attributed to higher denitrification rates under NT. Therefore, NT may be a viable strategy to remove NO₃⁻-N from the soil, and thus, reduce NO₃⁻-N contamination of groundwater. However, it should be noted that while the use of NT reduces NO₃⁻-N leaching it may come with unintended environmental tradeoffs, including increased NH₃ and N₂O emissions.     

A Review of the Use of Manure in Small-Scale Crop Production Systems in South Africa

ABSTRACT

This paper reviews the problems of soil fertility under small-scale crop production systems in KwaZulu-Natal province in South Africa. The role of manure in maintaining and replenishing soil fertility for crop production and the fate of manure once applied to the soil are reviewed and discussed. Special emphasis is placed on cattle and chicken manure and the role that soil texture plays in the mineralization of nitrogen (N) and phosphorus (P). In KwaZulu-Natal, small-scale farmers' maize (Zea mays L.) yields are between 1.0 and 1.5 mg ha^?1, which is very low compared with the maize potential yield of 4.5 mg ha^?1 for the area under small-scale farming conditions. A review of available literature on the use of manure for soil-fertility management showed that manure is a good source of plant nutrients. The use of manure is an old technology that is appropriate for small-scale farmers in South Africa, as most farmers practice mixed livestock and crop farming. Despite the use of manure dating back many years, small-scale farmers in South Africa are not fully exploiting the available manure for replenishing the fertility of their soils.     

Response of selected drought tolerant wheat (Triticum aestivum L.) genotypes for agronomic traits and biochemical markers under drought-stressed and non-stressed conditions

ABSTRACT

Genetic improvement of wheat for drought tolerance can be achieved by developing suitable ideotypes with enhanced yield-response associated with agronomic traits and biochemical markers. The objective of this study was to determine drought response of elite drought tolerant wheat genotypes using agronomic and biochemical traits to select promising lines for breeding. Fourteen wheat genotypes selected from the International Maize and Wheat Improvement Center’s heat and drought tolerance nursery and one standard check variety were evaluated under drought-stressed (DS) and non-stressed (NS) conditions using a randomised complete block design in three replications. Significant (P?<?0.05) genotype, drought condition and genotype?×?drought condition interaction effect were detected for the tested traits suggesting differential response of genotype for selection. Grain yield positively correlated with sucrose (r?=?0.58; P?<?0.05), fructose (r?=?0.52; P?<?0.05) and total sugar (r?=?0.52; P?<?0.05) contents under NS condition and with sucrose (r?=?0.80; P?<?0.001), total sugar (r?=?0.84; P?<?0.001) content, proline content (r?=?0.74; P?<?0.001) and number of grains per spike (r?=?0.58; P?<?0.05) under DS condition. Genetically unrelated wheat genotypes such as SM04, SM19, SM29, SM32, SM45 and SM97 possessing key agronomic and biochemical traits were selected for cultivar development for drought-stressed environments.     

Crop yield forecasting on the Canadian Prairies using MODIS NDVI data

Although Normalised Difference Vegetation Index (NDVI) data derived from the advanced very high resolution radiometer (AVHRR) sensor have been extensively used to assess crop condition and yield on the Canadian Prairies and elsewhere, NDVI data derived from the new moderate resolution imaging spectroradiometer (MODIS) sensor have so far not been used for crop yield prediction on the Canadian Prairies. Therefore, the objective of this study was to evaluate the possibility of using MODIS-NDVI to forecast crop yield on the Canadian Prairies and also to identify the best time for making a reliable crop yield forecast. Growing season (May-August) MODIS 10-day composite NDVI data for the years 2000-2006 were obtained from the Canada Centre for Remote Sensing (CCRS). Crop yield data (i.e., barley, canola, field peas and spring wheat) for each Census Agricultural Region (CAR) were obtained from Statistics Canada. Correlation and regression analyses were performed using 10-day composite NDVI and running average NDVI for 2, 3 and 4 dekads with the highest correlation coefficients (r) as the independent variables and crop grain yield as the dependent variable. To test the robustness and the ability of the generated regression models to forecast crops grain yield, one year at a time was removed and new regression models were developed, which were then used to predict the grain yield for the missing year. Results showed that MODIS-NDVI data can be used effectively to predict crop yield on the Canadian Prairies. Depending on the agro-climatic zone, the power function models developed for each crop accounted for 48 to 90%, 32 to 82%, 53 to 89% and 47 to 80% of the grain yield variability for barley, canola, field peas and spring wheat, respectively, with the best prediction in the semi-arid zone. Overall (54 out of 84), the % difference of the predicted from the actual grain yield was within ±10%. On the whole, RMSE values ranged from 150 to 654, 108 to 475, 204 to 677 and 104 to 714 kg ha⁻¹ for barley, canola, field peas and spring wheat, respectively. When expressed as percentages of actual yield, the RMSE values ranged from 8 to 25% for barley, 10 to 58% for canola, 10 to 38% for field peas and 6 to 34% for spring wheat. The MAE values followed a similar trend but were slightly lower than the RMSE values. For all the crops, the best time for making grain yield predictions was found to be from the third dekad of June through the third dekad of July in the sub-humid zone and from the first dekad of July through the first dekad of August in both the semi-arid and arid zones. This means that accurate crop grain yield forecasts using the developed regression models can be made one to two months before harvest.