Previous land use and climate influence differences in soil organic carbon following reforestation of agricultural land with mixed-species plantings

Reforestation of agricultural land with mixed-species environmental plantings (native trees and shrubs) can contribute to mitigation of climate change through sequestration of carbon. Although soil carbon sequestration following reforestation has been investigated at site- and regional-scales, there are few studies across regions where the impact of a broad range of site conditions and management practices can be assessed. We collated new and existing data on soil organic carbon (SOC, 0–30 cm depth, N = 117 sites) and litter (N = 106 sites) under mixed-species plantings and an agricultural pair or baseline across southern and eastern Australia. Sites covered a range of previous land uses, initial SOC stocks, climatic conditions and management types. Differences in total SOC stocks following reforestation were significant at 52% of sites, with a mean rate of increase of 0.57 ± 0.06 Mg C ha−1 y−1. Increases were largely in the particulate fraction, which increased significantly at 46% of sites compared with increases at 27% of sites for the humus fraction. Although relative increase was highest in the particulate fraction, the humus fraction was the largest proportion of total SOC and so absolute differences in both fractions were similar. Accumulation rates of carbon in litter were 0.39 ± 0.02 Mg C ha−1 y−1, increasing the total (soil + litter) annual rate of carbon sequestration by 68%. Previously-cropped sites accumulated more SOC than previously-grazed sites. The explained variance differed widely among empirical models of differences in SOC stocks following reforestation according to SOC fraction and depth for previously-grazed (R2 = 0.18–0.51) and previously-cropped (R2 = 0.14–0.60) sites. For previously-grazed sites, differences in SOC following reforestation were negatively related to total SOC in the pasture. By comparison, for previously-cropped sites, differences in SOC were positively related to mean annual rainfall. This improved broad-scale understanding of the magnitude and predictors of changes in stocks of soil and litter C following reforestation is valuable for the development of policy on carbon markets and the establishment of future mixed-species environmental plantings.

Optimised wheat root architecture for increased yield and yield stability in the face of a changing climate.

This project provided information, selection techniques and strategies to facilitate the development of high-yielding, stay-green wheat varieties for Australian growers through: a) Improved understanding of the relationships between seminal root traits and other root- and shoot-related traits in determining high-yielding, stay-green phenotypes. b). Molecular markers and rapid phenotypic screening methods that allow selection in breeding programs and identification of genetic regions controlling favourable traits. c). Identification of traits leading to high-yielding, stay-green phenotypes for particular target populations of environments using computer simulation studies.

Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support

As climate change continues to impact socio-ecological systems, tools that assist conservation managers to understand vulnerability and target adaptations are essential. Quantitative assessments of vulnerability are rare because available frameworks are complex and lack guidance for dealing with data limitations and integrating across scales and disciplines. This paper describes a semi-quantitative method for assessing vulnerability to climate change that integrates socio-ecological factors to address management objectives and support decision-making. The method applies a framework first adopted by the Intergovernmental Panel on Climate Change and uses a structured 10-step process. The scores for each framework element are normalized and multiplied to produce a vulnerability score and then the assessed components are ranked from high to low vulnerability. Sensitivity analyses determine which indicators most influence the analysis and the resultant decision-making process so data quality for these indicators can be reviewed to increase robustness. Prioritisation of components for conservation considers other economic, social and cultural values with vulnerability rankings to target actions that reduce vulnerability to climate change by decreasing exposure or sensitivity and/or increasing adaptive capacity. This framework provides practical decision-support and has been applied to marine ecosystems and fisheries, with two case applications provided as examples: (1) food security in Pacific Island nations under climate-driven fish declines, and (2) fisheries in the Gulf of Carpentaria, northern Australia. The step-wise process outlined here is broadly applicable and can be undertaken with minimal resources using existing data, thereby having great potential to inform adaptive natural resource management in diverse locations.

Quantifying nitrous oxide emissions from sugarcane cropping systems : Optimum sampling time and frequency

Nitrous oxide (N2O) emissions from soil are often measured using the manual static chamber method. Manual gas sampling is labour intensive, so a minimal sampling frequency that maintains the accuracy of measurements would be desirable. However, the high temporal (diurnal, daily and seasonal) variabilities of N2O emissions can compromise the accuracy of measurements if not addressed adequately when formulating a sampling schedule. Assessments of sampling strategies to date have focussed on relatively low emission systems with high episodicity, where a small number of the highest emission peaks can be critically important in the measurement of whole season cumulative emissions. Using year-long, automated sub-daily N2O measurements from three fertilised sugarcane fields, we undertook an evaluation of the optimum gas sampling strategies in high emission systems with relatively long emission episodes. The results indicated that sampling in the morning between 09:00–12:00, when soil temperature was generally close to the daily average, best approximated the daily mean N2O emission within 4–7% of the ‘actual’ daily emissions measured by automated sampling. Weekly sampling with biweekly sampling for one week after >20 mm of rainfall was the recommended sampling regime. It resulted in no extreme (>20%) deviations from the ‘actuals’, had a high probability of estimating the annual cumulative emissions within 10% precision, with practicable sampling numbers in comparison to other sampling regimes. This provides robust and useful guidance for manual gas sampling in sugarcane cropping systems, although further adjustments by the operators in terms of expected measurement accuracy and resource availability are encouraged. By implementing these sampling strategies together, labour inputs and errors in measured cumulative N2O emissions can be minimised. Further research is needed to quantify the spatial variability of N2O emissions within sugarcane cropping and to develop techniques for effectively addressing both spatial and temporal variabilities simultaneously.

Stay-green Drought Adaptation Trait Enhances Sorghum Production in Sub-tropical Australia, Central-Western India and Sub-Saharan Africa

Drought during grain filling is a common challenge for sorghum production in north-eastern Australia, central-western India, and sub-Saharan Africa. We show that the stay-green drought adaptation trait enhances sorghum grain yield under post-anthesis drought in these three regions. A positive relationship between stay-green and yield was generally found in breeding trials in north-eastern Australia that sampled 1668 unique hybrid combinations and 23 environments. Physiological studies in Australia also found that introgressing four individual stay-green (Stg1–4) quantitative trait loci (QTLs) into a senescent background reduced water demand before flowering and hence increased water supply during grain filling, resulting in higher grain yield relative to the senescent control. Studies in India found that various Stg QTLs affected both transpiration and transpiration efficiency, although these effects depended on the interaction between genetic background (S35 and R16) and individual QTLs. The yield variation unexplained by harvest index was related to transpiration efficiency in S35 (R2 = 0.29) and R16 (R2 = 0.72), and was related to total water extracted in S35 (R2 = 0.41) but not in R16. Finally, sixty-eight stay-green enriched lines were evaluated in six countries in sub-Saharan Africa during the 2013/14 season. Analysis of the data from Kenya indicates that stay-green and grain size were positively correlated at two sites: Kiboko (high yielding, r2=0.25) and Masongaleni (low yielding, r2=0.37). Together, these studies suggest that stay-green is a beneficial trait for sorghum production in the semi-arid tropics and is a consequence of traits altering the plant water budget.

Resource use and greenhouse gas emissions from grain-finishing beef cattle in seven Australian feedlots: a life cycle assessment

Grain finishing of cattle has become increasingly common in Australia over the past 30 years. However, interest in the associated environmental impacts and resource use is increasing and requires detailed analysis. In this study we conducted a life cycle assessment (LCA) to investigate impacts of the grain-finishing stage for cattle in seven feedlots in eastern Australia, with a particular focus on the feedlot stage, including the impacts from producing the ration, feedlot operations, transport, and livestock emissions while cattle are in the feedlot (gate-to-gate). The functional unit was 1 kg of liveweight gain (LWG) for the feedlot stage and results are included for the full supply chain (cradle-to-gate), reported per kilogram of liveweight (LW) at the point of slaughter. Three classes of cattle produced for different markets were studied: short-fed domestic market (55–80 days on feed), mid-fed export (108–164 days on feed) and long-fed export (>300 days on feed). In the feedlot stage, mean fresh water consumption was found to vary from 171.9 to 672.6 L/kg LWG and mean stress-weighted water use ranged from 100.9 to 193.2 water stress index eq. L/kg LWG. Irrigation contributed 57–91% of total fresh water consumption with differences mainly related to the availability of irrigation water near the feedlot and the use of irrigated feed inputs in rations. Mean fossil energy demand ranged from 16.5 to 34.2 MJ lower heating values/kg LWG and arable land occupation from 18.7 to 40.5 m2/kg LWG in the feedlot stage. Mean greenhouse gas (GHG) emissions in the feedlot stage ranged from 4.6 to 9.5 kg CO2-e/kg LWG (excluding land use and direct land-use change emissions). Emissions were dominated by enteric methane and contributions from the production, transport and milling of feed inputs. Linear regression analysis showed that the feed conversion ratio was able to explain >86% of the variation in GHG intensity and energy demand. The feedlot stage contributed between 26% and 44% of total slaughter weight for the classes of cattle fed, whereas the contribution of this phase to resource use varied from 4% to 96% showing impacts from the finishing phase varied considerably, compared with the breeding and backgrounding. GHG emissions and total land occupation per kilogram of LWG during the grain finishing phase were lower than emissions from breeding and backgrounding, resulting in lower life-time emissions for grain-finished cattle compared with grass finishing.

Farm practices to manage the impact of severe tropical cyclone damage on banana production – a case study from tropical Australia

Most Australian banana production occurs on the north-eastern tropical coast between latitudes 15-18°S, and can experience summer cyclone activity. Damage from severe tropical cyclones has serious impact on banana-based livelihoods. The most significant impacts include immediate loss of production and income for several months, the region-wide synchronization of cropping and the expense of rehabilitating affected plantations. Severe tropical cyclones have directly affected the main production region twice in recent years Tropical Cyclone (TC) Larry (Category 4) in March 2006 and TC Yasi (Category 5) in February 2011. Based on TC Larry experiences, pre- and post-cyclone farm practices were developed to reduce these impacts in future cyclonic events. The main pre-cyclone farm practice focused on maintaining production units and an earlier return to fruit production by partially or completely removing the plant canopy to reduce wind resistance. Post-cyclone farm practices focused on managing the industry-wide crop synchronization using crop timing techniques to achieve a staggered return to cropping by scheduling production to provide continuous fruit supply. With TC Yasi in 2011, some banana producers implemented these practices, allowing them to examine their effectiveness in reducing cyclonic impacts. Additional research and development activities were conducted to refine our understanding of their effectiveness and improve their application for future cyclonic events. Based on these activities and farm-based observations, suggested practice-based management strategies can be developed to help reduce the impact of severe tropical cyclones in the future. Canopy removal maintained banana plants as productive units, and provided earlier but smaller bunches, generating earlier-than-expected income. Queensland producers expressed willingness to adopt canopy removal for future cyclone threats where appropriate, despite its labor-intensiveness. Mechanization would allow larger scale adoption. Implementing a staggered cropping program successfully achieved a consistent, continuous fruit supply after a cyclone impact. Both techniques should be applicable to other cyclone-prone regions.

Protected cropping of strawberry plants in subtropical Queensland

The effect of protected cropping on the performance of two strawberry cultivars ('Festival' and 'Rubygem') and two breeding lines (Breeding Lines 1 and 2) was studied in subtropical Queensland, Australia over two years. Production in this area is affected by rain, with direct damage to the fruit and the development of fruit diseases before harvest. The main objective of the study was to determine whether plants grown under high plastic tunnels had less rain damage, less disease incidence, and higher yields than plants grown outdoors. Our studies show that marketable yields were up to 40% higher in the plants under the tunnels compared with yields of the plants outdoors. This was mainly because fruit from the plants grown under the tunnels had lower incidences of rain damage and/or grey mould. There were no consistent differences in the relative numbers of small and/or misshaped fruit in the two growing environments. This research highlights the potential of protected cropping for strawberry producers in subtropical areas that receive significant rainfall during the growing season.

Bioeconomic modelling of woody regrowth carbon offset options in productive grazing systems

Agricultural land has been identified as a potential source of greenhouse gas emissions offsets through biosequestration in vegetation and soil. In the extensive grazing land of Australia, landholders may participate in the Australian Government’s Emissions Reduction Fund and create offsets by reducing woody vegetation clearing and allowing native woody plant regrowth to grow. This study used bioeconomic modelling to evaluate the trade-offs between an existing central Queensland grazing operation, which has been using repeated tree clearing to maintain pasture growth, and an alternative carbon and grazing enterprise in which tree clearing is reduced and the additional carbon sequestered in trees is sold. The results showed that ceasing clearing in favour of producing offsets produces a higher net present value over 20 years than the existing cattle enterprise at carbon prices, which are close to current (2015) market levels (~$13 t–1 CO2-e). However, by modifying key variables, relative profitability did change. Sensitivity analysis evaluated key variables, which determine the relative profitability of carbon and cattle. In order of importance these were: the carbon price, the gross margin of cattle production, the severity of the tree–grass relationship, the area of regrowth retained, the age of regrowth at the start of the project, and to a lesser extent the cost of carbon project administration, compliance and monitoring. Based on the analysis, retaining regrowth to generate carbon income may be worthwhile for cattle producers in Australia, but careful consideration needs to be given to the opportunity cost of reduced cattle income.

Weekly weather and crop bulletin /

Latest issue consulted: Vol. 85, no. 30 (July 28, 1998).

Weather, crops and markets ...

Mode of access: Internet.

The application of agricultural land rating and crop models to CO2 and climate change issues in Northern regions : the Mackenzie Basin case study

Selostus: Viljelyvyöhykkeiden ja kasvumallien soveltaminen ilmastonmuutoksen tutkimisessa: Mackenzien jokialue, Kanada

Methods and resources for climate impacts research: achieving synergy

The prediction of climate variability and change requires the use of a range of simulation models. Multiple climate model simulations are needed to sample the inherent uncertainties in seasonal to centennial prediction. Because climate models are computationally expensive, there is a tradeoff between complexity, spatial resolution, simulation length, and ensemble size. The methods used to assess climate impacts are examined in the context of this trade-off. An emphasis on complexity allows simulation of coupled mechanisms, such as the carbon cycle and feedbacks between agricultural land management and climate. In addition to improving skill, greater spatial resolution increases relevance to regional planning. Greater ensemble size improves the sampling of probabilities. Research from major international projects is used to show the importance of synergistic research efforts. The primary climate impact examined is crop yield, although many of the issues discussed are relevant to hydrology and health modeling. Methods used to bridge the scale gap between climate and crop models are reviewed. Recent advances include large-area crop modeling, quantification of uncertainty in crop yield, and fully integrated crop–climate modeling. The implications of trends in computer power, including supercomputers, are also discussed.

Adaptation of crops to climate change through genotypic responses to mean and extreme temperatures

The importance of temperature in the determination of the yield of an annual crop (groundnut; Arachis hypogaea L. in India) was assessed. Simulations from a regional climate model (PRECIS) were used with a crop model (GLAM) to examine crop growth under simulated current (1961-1990) and future (2071-2100) climates. Two processes were examined: the response of crop duration to mean temperature and the response of seed-set to extremes of temperature. The relative importance of, and interaction between, these two processes was examined for a number of genotypic characteristics, which were represented by using different values of crop model parameters derived from experiments. The impact of mean and extreme temperatures varied geographically, and depended upon the simulated genotypic properties. High temperature stress was not a major determinant of simulated yields in the current climate, but affected the mean and variability of yield under climate change in two regions which had contrasting statistics of daily maximum temperature. Changes in mean temperature had a similar impact on mean yield to that of high temperature stress in some locations and its effects were more widespread. Where the optimal temperature for development was exceeded, the resulting increase in duration in some simulations fully mitigated the negative impacts of extreme temperatures when sufficient water was available for the extended growing period. For some simulations the reduction in mean yield between the current and future climates was as large as 70%, indicating the importance of genotypic adaptation to changes in both means and extremes of temperature under climate change. (c) 2006 Elsevier B.V. All rights reserved.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.