De Li Liu

Adapting agriculture to climate change: a review

The agricultural sector is highly vulnerable to future climate changes and climate variability, including increases in the incidence of extreme climate events. Changes in temperature and precipitation will result in changes in land and water regimes that will subsequently affect agricultural productivity. Given the gradual change of climate in the past, historically, farmers have adapted in an autonomous manner. However, with large and discrete climate change anticipated by the end of this century, planned and transformational changes will be needed. In light of these, the focus of this review is on farm-level and farmers responses to the challenges of climate change both spatially and over time. In this review of adapting agriculture to climate change, the nature, extent, and causes of climate change are analyzed and assessed. These provide the context for adapting agriculture to climate change. The review identifies the binding constraints to adaptation at the farm level. Four major priority areas are identified to relax these constraints, where new initiatives would be required, i.e., information generation and dissemination to enhance farm-level awareness, research and development (R&D) in agricultural technology, policy formulation that facilitates appropriate adaptation at the farm level, and strengthening partnerships among the relevant stakeholders. Forging partnerships among R&D providers, policy makers, extension agencies, and farmers would be at the heart of transformational adaptation to climate change at the farm level. In effecting this transformational change, sustained efforts would be needed for the attendant requirements of climate and weather forecasting and innovation, farmer’s training, and further research to improve the quality of information, invention, and application in agriculture. The investment required for these would be highly significant. The review suggests a sequenced approach through grouping research initiatives into short-term, medium-term, and long-term initiatives, with each initiative in one stage contributing to initiatives in a subsequent stage. The learning by doing inherent in such a process-oriented approach is a requirement owing to the many uncertainties associated with climate change.

Climate and soil properties limit the positive effects of land use reversion on carbon storage in Eastern Australia

Australia’s “Direct Action” climate change policy relies on purchasing greenhouse gas abatement from projects undertaking approved abatement activities. Management of soil organic carbon (SOC) in agricultural soils is an approved activity, based on the expectation that land use change can deliver significant changes in SOC. However, there are concerns that climate, topography and soil texture will limit changes in SOC stocks. This work analyses data from 1482 sites surveyed across the major agricultural regions of Eastern Australia to determine the relative importance of land use vs. other drivers of SOC. Variation in land use explained only 1.4% of the total variation in SOC, with aridity and soil texture the main regulators of SOC stock under different land uses. Results suggest the greatest potential for increasing SOC stocks in Eastern Australian agricultural regions lies in converting from cropping to pasture on heavy textured soils in the humid regions.

Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.

Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia

Projections of changes in temperature extremes are critical to assess the potential impacts of climate change on agricultural and ecological systems. Statistical downscaling can be used to efficiently downscale output from a large number of general circulation models (GCMs) to a fine temporal and spatial scale, providing the opportunity for future projections of extreme temperature events. This paper presents an analysis of extreme temperature data downscaled from 7 GCMs selected from the Coupled Model Intercomparison Project phase 5 (CMIP5) using a skill score based on spatial patterns of climatological means of daily maximum and minimum temperature. Data for scenarios RCP4.5 and RCP8.5 for the New South Wales (NSW) wheat belt, south eastern Australia, have been analysed. The results show that downscaled data from most of the GCMs reproduces the correct sign of recent trends in all the extreme temperature indices (except the index for cold days) for 1961–2000. An independence weighted mean method is used to calculate uncertainty estimates, which shows that multi-model ensemble projections produce a consistent trend compared to the observations in most extreme indices. Great warming occurs in the east and northeast of the NSW wheat belt by 2061–2100 and increases the risk of exposure to hot days around wheat flowering date, which might result in farmers needing to reconsider wheat varieties suited to maintain yield. This analysis provides a first overview of projected changes in climate extremes from an ensemble of 7 CMIP5 GCM models with statistical downscaling data in the NSW wheat belt.

Tillage does not increase nitrous oxide emissions under dryland canola (Brassica napus L.) in a semiarid environment of south-eastern Australia

Dryland cereal production systems of south-eastern Australia require viable options for reducing nitrous oxide (N2O) emissions without compromising productivity and profitability. A 4-year rotational experiment with wheat (Triticum aestivum L.)–canola (Brassica napus L.)–grain legumes–wheat in sequence was established at Wagga Wagga, NSW, Australia, in a semiarid Mediterranean-type environment where long-term average annual rainfall is 541mm and the incidence of summer rainfall is episodic and unreliable. The objectives of the experiment were to investigate whether (i) tillage increases N2O emissions and (ii) nitrogen (N) application can improve productivity without increasing N2O emissions. The base experimental design for each crop phase was a split-plot design with tillage treatment (tilled versus no-till) as the whole plot, and N fertiliser rate (0, 25, 50 and 100kgN/ha) as the subplot, replicated three times. This paper reports high resolution N2O emission data under a canola crop. The daily N2O emission rate averaged 0.55g N2O-N/ha.day, ranging between –0.81 and 6.71g N2O-N/ha.day. The annual cumulative N2O-N emitted was 175.6 and 224.3g N2O-N/ha under 0 and 100kgN/ha treatments respectively. There was no evidence to support the first hypothesis that tillage increases N2O emissions, a result which may give farmers more confidence to use tillage strategically to manage weeds and diseases where necessary. However, increasing N fertiliser rate tended to increase N2O emissions, but did not increase crop production at this site.

Regional Water Balance Based on Remotely Sensed Evapotranspiration and Irrigation: An Assessment of the Haihe Plain, China

Optimal planning and management of the limited water resources for maximum productivity in agriculture requires quantifying the irrigation applied at a regional scale. However, most efforts involving remote sensing applications in assessing large-scale irrigation applied (IA) have focused on supplying spatial variables for crop models or studying evapotranspiration (ET) inversions, rather than directly building a remote sensing data-based model to estimate IA. In this study, based on remote sensing data, an IA estimation model together with an ET calculation model (ETWatch) is set up to simulate the spatial distribution of IA in the Haihe Plain of northern China. We have verified this as an effective approach for the simulation of regional IA, being more reflective of regional characteristics and of higher resolution compared to single site-specific results. The results show that annual ET varies from 527 mm to 679 mm and IA varies from 166 mm to 289 mm, with average values of 602 mm and 225 mm, respectively, from 2002 to 2007. We confirm that the region along the Taihang Mountain in Hebei Plain has serious water resource sustainability problems, even while receiving water from the South-North Water Transfer (SNWT) project. This is due to the region’s intensive agricultural production and declining groundwater tables. Water-saving technologies, including more timely and accurate geo-specific IA assessments, may help reduce this threat.

Confidence in soil carbon predictions undermined by the uncertainties in observations and model parameterisation

Soil carbon (C) responds quickly and feedbacks significantly to environmental changes such as climate warming and agricultural management. Soil C modelling is the only reasonable approach available for predicting soil C dynamics under future conditions of environmental changes, and soil C models are usually constrained by the average of observations. However, model constraining is sensitive to the observed data, and the consequence of using observed averages on C predictions has rarely been studied. Using long-term soil organic C datasets from an agricultural field experiment, we constrained a process-based model using the average of observations or by taking into account the variation in observations to predict soil C dynamics. We found that uncertainties in soil C predictions were masked if ignoring the uncertainties in observations (i.e., using the average of observations to constrain model), if uncertainties in model parameterisation were not explicitly quantified. However, if uncertainties in model parameterisation had been considered, further considering uncertainties in observations had negligible effect on uncertainties in SOC predictions. The results suggest that uncertainties induced by model parameterisation are larger than that induced by observations. Precise observations representing the real spatial pattern of SOC at the studied domain, and model structure improvement and constrained space of parameters will benefit reducing uncertainties in soil C predictions. The results also highlight some areas on which future C model development and software implementations should focus to reliably infer soil C dynamics. We simulate observed soil carbon dynamics using different calibration strategies.Different model initialisation and/or parameterisation cause divergent predictions.Ignoring uncertainty in observations results in biased predictions.Robust prediction needs accurate observations and constrained parameter space.

Modeling the impact of crop rotation with legume on nitrous oxide emissions from rain-fed agricultural systems in Australia under alternative future climate scenarios

Limited information exists on potential impacts of climate change on nitrous oxide (N2O) emissions by including N2-fixing legumes in crop rotations from rain-fed cropping systems. Data from two 3-yr crop rotations in northern NSW, Australia, viz. chickpea-wheat-barley (CpWB) and canola-wheat-barley (CaWB), were used to gain an insight on the role of legumes in mitigation of N2O emissions. High-frequency N2O fluxes measured with an automated system of static chambers were utilized to test the applicability of Denitrification and Decomposition model. The DNDC model was run using the on-site observed weather, soil and farming management conditions as well as the representative concentration pathways adopted by the Intergovernmental Panel on Climate Change in its Fifth Assessment Report. The DNDC model captured the cumulative N2O emissions with variations falling within the deviation ranges of observations (0.88±0.31kgNha-1rotation-1 for CpWB, 1.44±0.02kgNha-1rotation-1 for CaWB). The DNDC model can be used to predict between modeled and measured N2O flux values for CpWB (n=390, RSR=0.45) and CaWB (n=390, RSR=0.51). Long-term (80-yr) simulations were conducted with RCP 4.5 representing a global greenhouse gas stabilization scenario, as well RCP 8.5 representing a very high greenhouse gas emission scenario based on RCP scenarios. Compared with the baseline scenarios for CpWB and CaWB, the long-term simulation results under RCP scenarios showed that, (1) N2O emissions would increase by 35-44% for CpWB and 72-76% for CaWB under two climate scenarios; (2) grain yields would increase by 9% and 18% under RCP 4.5, and 2% and 14% under RCP 8.5 for CpWB and CaWB, respectively; and (3) yield-scaled N2O-N emission would increase by 24-42% for CpWB and 46-54% for CaWB under climate scenarios, respectively. Our results suggest that 25% of the yield-scaled N2O-N emission would be saved by switching to a legume rotation under climate change conditions.

High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia

Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are central in understanding the global carbon cycle and informing related land management decisions. However, mapping SOC stocks in semi-arid rangelands is challenging due to the lack of data and poor spatial coverage. The use of remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide more reliable and cost-effective estimates of SOC compared with field-based, direct measurement. Despite this potential, the role of remote sensing data in improving the knowledge of soil information in semi-arid rangelands has not been fully explored. This study firstly investigated the use of high spatial resolution satellite data (seasonal fractional cover data; SFC) together with elevation, lithology, climatic data and observed soil data to map the spatial distribution of SOC at two soil depths (0-5cm and 0-30cm) in semi-arid rangelands of eastern Australia. Overall, model performance statistics showed that random forest (RF) and boosted regression trees (BRT) models performed better than support vector machine (SVM). The models obtained moderate results with R2 of 0.32 for SOC stock at 0-5cm and 0.44 at 0-30cm, RMSE of 3.51MgCha-1 at 0-5cm and 9.16MgCha-1 at 0-30cm without considering SFC covariates. In contrast, by including SFC, the model accuracy for predicting SOC stock improved by 7.4-12.7% at 0-5cm, and by 2.8-5.9% at 0-30cm, highlighting the importance of including SFC to enhance the performance of the three modelling techniques. Furthermore, our models produced a more accurate and higher resolution digital SOC stock map compared with other available mapping products for the region. The data and high-resolution maps from this study can be used for future soil carbon assessment and monitoring.

Pedo-transfer functions for estimating the hydraulic properties of paddy soils in subtropical central China

ABSTRACT Pedo-transfer functions (PTFs) have been widely used to estimate soil hydraulic properties in the simulation of catchment eco-hydrological processes. However, the accuracy of existing PTFs is usually inadequate for use. To develop PTFs for local use, soil columns were collected from a double rice-cropped agricultural catchment in subtropical central China. The PTFs for saturated soil hydraulic conductivity (Ks) and parameters (θs, α, and n) of the van Genuchten model for the soil water retention curve (SWRC) were obtained based on soil’s basic properties, and compared with models developed by Li et al. in 2007 and Wösten et al. in 1999, respectively. Our results indicated that Ks in the range of 0.04–1087 cm d−1 and θs in the range of 0.34–0.51 cm3 cm−3 were both well estimated with the R2adj of 0.72 and 0.87, respectively, but α (0.04–0.65 cm−1) and n (1.05–1.21) were relatively poorly predicted with the respective R2adj of 0.38 and 0.55, despite the use of more input parameters. Our local derived PTFs outperformed the other two existing models. However, if the local PTFs for paddy soils are not available, the Wösten et al. 1999 model can be proposed as a useful alternative. Therefore, this study can improve our understanding of the development and application of PTFs for predicting paddy soil hydraulic properties in China.