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In recent years, the occurrence of floods is one of the most important challenges facing in Hamadan city. In the absence/inefficiency of urban drainage systems, rainwater harvesting (RWH) systems as low-impact development (LID) methods can be considered as a measure to reduce the floods. In this study, three scenarios concerning the RWH from the roof surfaces are studied to evaluate the type of the harvested water on reducing flooding. In the first scenario, which indicates the current situation in the studied area, it is indicated that there is no harvest of the roof surfaces in the studied area. The second scenario is about the use of water harvested from the roof surfaces for household purposes. The third scenario also refers to the use of harvested water for irrigation of gardens. The simulation results of these three scenarios using the Soil Conservation Service (SCS) method in the Hydrologic Modeling System (HEC-HMS) model reveal that if the second scenario is implemented, the runoff volume decreases from 28 to 12% for the return period from 2 to 100 years. However, in the third scenario, this reduction in runoff volume will be 48 and 27% for return periods of 2 to 100 years, respectively. Therefore, the results of this study indicate that the use of harvested water can also affect the reduction on runoff volume.
Sarita Gajbhiye Meshram; Ali Reza Ilderomi; Mehdi Sepehri; Farshid Jahanbakhshi; Mahboobeh Kiani-Harchegani; Afshin Ghahramani; Jesús Rodrigo-Comino. Impact of roof rain water harvesting of runoff capture and household consumption. Environmental Science and Pollution Research 2021, 1 -12.
AMA StyleSarita Gajbhiye Meshram, Ali Reza Ilderomi, Mehdi Sepehri, Farshid Jahanbakhshi, Mahboobeh Kiani-Harchegani, Afshin Ghahramani, Jesús Rodrigo-Comino. Impact of roof rain water harvesting of runoff capture and household consumption. Environmental Science and Pollution Research. 2021; ():1-12.
Chicago/Turabian StyleSarita Gajbhiye Meshram; Ali Reza Ilderomi; Mehdi Sepehri; Farshid Jahanbakhshi; Mahboobeh Kiani-Harchegani; Afshin Ghahramani; Jesús Rodrigo-Comino. 2021. "Impact of roof rain water harvesting of runoff capture and household consumption." Environmental Science and Pollution Research , no. : 1-12.
Long-term forecasting of hydrologic phenomena is essential for strategic environmental planning, hydrologic structural design, agriculture, and water resources management. Climate mode indices are frequently considered for forecast hydrological variables using conventional machine learning. In this study, a feature selection algorithm with two deep learning models, i.e., long short-term memory and a gated recurrent unit, is applied to improve the forecasting capacity of streamflow water levels for six gauging stations of Murray Darling Basin of Australia. This paper aggregated the significant decedent lag memories of climate mode indices, rainfall, and monthly factor of periodicity as predictors to attain a significantly accurate stream water level forecast. This method identifies an improved relationship between the stream water level and climate indices through the aggregation of the significant lags. Boruta feature selection algorithm (BRF) was applied in two phases before and after attaining significant lags to screen the optimum predictors. The merit of the forecasted models was assessed through different performance evaluation criteria. Results show that accumulated significant lags of climate mode indices along with rainfall and periodicity factors provide improved forecasting of SWL over the non-BRF deep learning approaches. The hybrid LSTM (BRF-LSTM) model achieved a unique advantage for SWL forecasting with over 98% predictive errors lying within 0.015 m and low relative error (RRMSE ≈1.30% and RMAE ≈ 0.882%), outperforming all of the examined benchmark models. It is also found that the periodicity factor shows a potential influence on four monitored stations. Moreover, rainfall has a substantial impact on four stations. This study concludes that the hybrid deep learning approaches, coupled with BRF feature selection, provide improved forecasting performance. The hybrid approach developed in this paper can be used to forecast the response of the hydrological variables influenced by the low-frequency variability of climate indices.
A.A. Masrur Ahmed; Ravinesh C. Deo; Qi Feng; Afshin Ghahramani; Nawin Raj; Zhenliang Yin; Linshan Yang. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity. Journal of Hydrology 2021, 126350 .
AMA StyleA.A. Masrur Ahmed, Ravinesh C. Deo, Qi Feng, Afshin Ghahramani, Nawin Raj, Zhenliang Yin, Linshan Yang. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity. Journal of Hydrology. 2021; ():126350.
Chicago/Turabian StyleA.A. Masrur Ahmed; Ravinesh C. Deo; Qi Feng; Afshin Ghahramani; Nawin Raj; Zhenliang Yin; Linshan Yang. 2021. "Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity." Journal of Hydrology , no. : 126350.
Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.
A. Ahmed; Ravinesh Deo; Nawin Raj; Afshin Ghahramani; Qi Feng; Zhenliang Yin; Linshan Yang. Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data. Remote Sensing 2021, 13, 554 .
AMA StyleA. Ahmed, Ravinesh Deo, Nawin Raj, Afshin Ghahramani, Qi Feng, Zhenliang Yin, Linshan Yang. Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data. Remote Sensing. 2021; 13 (4):554.
Chicago/Turabian StyleA. Ahmed; Ravinesh Deo; Nawin Raj; Afshin Ghahramani; Qi Feng; Zhenliang Yin; Linshan Yang. 2021. "Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data." Remote Sensing 13, no. 4: 554.
Future soil moisture (SM) estimation is a practically useful task for eco-hydrologists, agriculturists, and stakeholders in environment health monitoring to generate comprehensive understanding of hydro-physical and soil dynamic system. This paper demonstrates the capability of a hybridised long short-term memory (LSTM) predictive framework to emulate SM under global warming scenarios. The proposed model is developed by integrating Boruta-random forest (BRF) feature selection and capturing significant antecedent memory of SM behaviour were applied to estimate the future SM using Coupled Model Intercomparison Phase-5 (CMIP5) repository. The BRF is adapted to extract pertinent features in hydro-meteorological variables intrinsically related to SM, and therefore, is used to construct a hybridised deep learning (i.e., BRF-LSTM) model. To establish the viability of deep learning model for SM estimation until 2100, five stations closely matched to the global climate model grid are selected in Australia's Murray Darling Basin. The performance skill of BRF-LSTM model is compared against standalone models (i.e., LSTM, SVR, and MARS). The results showed that the hybrid deep learning model (i.e., BRF-LSTM) with a feature selection capability could significantly outperform the standalone models for both warming simulations. The proposed hybrid model also demonstrated superiority in SM estimation with over 95% of all predictive errors lying below 0.02 mm, and low relative root means square error (≈ 1.06% for RCP4.5 and ≈ 1.888% for RCP8.5) to outperform all the benchmark models. This study demonstrates the capability of LSTM algorithm coupled with BRF feature selection to simulate future soil moisture under climate change, and so, can be successfully implemented in hydrology, agriculture, soil use management and environmental management.
A. A. Masrur Ahmed; Ravinesh C. Deo; Afshin Ghahramani; Nawin Raj; Qi Feng; Zhenliang Yin; Linshan Yang. LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios. Stochastic Environmental Research and Risk Assessment 2021, 1 -31.
AMA StyleA. A. Masrur Ahmed, Ravinesh C. Deo, Afshin Ghahramani, Nawin Raj, Qi Feng, Zhenliang Yin, Linshan Yang. LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios. Stochastic Environmental Research and Risk Assessment. 2021; ():1-31.
Chicago/Turabian StyleA. A. Masrur Ahmed; Ravinesh C. Deo; Afshin Ghahramani; Nawin Raj; Qi Feng; Zhenliang Yin; Linshan Yang. 2021. "LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios." Stochastic Environmental Research and Risk Assessment , no. : 1-31.
Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. Highlights We propose a new approach to deriving calibration recommendations for system models Approach is based on analyzing calibration in multi-model simulation exercises Resulting recommendations are holistic and anchored in actual practice We apply the approach to calibration of crop models used to simulate phenology Recommendations concern: objective function, parameters to estimate, software used
Daniel Wallach; Taru Palosuo; Peter Thorburn; Zvi Hochman; Emmanuelle Gourdain; Fety Andrianasolo; Senthold Asseng; Bruno Basso; Samuel Buis; Neil Crout; Camilla Dibari; Benjamin Dumont; Roberto Ferrise; Thomas Gaiser; Cecile Garcia; Sebastian Gayler; Afshin Ghahramani; Santosh Hiremath; Steven Hoek; Heidi Horan; Gerrit Hoogenboom; Mingxia Huang; Mohamed Jabloun; Per-Erik Jansson; Qi Jing; Eric Justes; Kurt Christian Kersebaum; Anne Klosterhalfen; Marie Launay; Elisabet Lewan; Qunying Luo; Bernardo Maestrini; Henrike Mielenz; Marco Moriondo; Hasti Nariman Zadeh; Gloria Padovan; Jørgen Eivind Olesen; Arne Poyda; Eckart Priesack; Johannes Wilhelmus Maria Pullens; Budong Qian; Niels Schuetze; Vakhtang Shelia; Amir Souissi; Xenia Specka; Amit Kumar Srivastava; Tommaso Stella; Thilo Streck; Giacomo Trombi; Evelyn Wallor; Jing Wang; Tobias K.D. Weber; Lutz Weihermueller; Allard de Wit; Thomas Woehling; Liujun Xiao; Chuang Zhao; Yan Zhu; Sabine J. Seidel. The chaos in calibrating crop models. 2020, 1 .
AMA StyleDaniel Wallach, Taru Palosuo, Peter Thorburn, Zvi Hochman, Emmanuelle Gourdain, Fety Andrianasolo, Senthold Asseng, Bruno Basso, Samuel Buis, Neil Crout, Camilla Dibari, Benjamin Dumont, Roberto Ferrise, Thomas Gaiser, Cecile Garcia, Sebastian Gayler, Afshin Ghahramani, Santosh Hiremath, Steven Hoek, Heidi Horan, Gerrit Hoogenboom, Mingxia Huang, Mohamed Jabloun, Per-Erik Jansson, Qi Jing, Eric Justes, Kurt Christian Kersebaum, Anne Klosterhalfen, Marie Launay, Elisabet Lewan, Qunying Luo, Bernardo Maestrini, Henrike Mielenz, Marco Moriondo, Hasti Nariman Zadeh, Gloria Padovan, Jørgen Eivind Olesen, Arne Poyda, Eckart Priesack, Johannes Wilhelmus Maria Pullens, Budong Qian, Niels Schuetze, Vakhtang Shelia, Amir Souissi, Xenia Specka, Amit Kumar Srivastava, Tommaso Stella, Thilo Streck, Giacomo Trombi, Evelyn Wallor, Jing Wang, Tobias K.D. Weber, Lutz Weihermueller, Allard de Wit, Thomas Woehling, Liujun Xiao, Chuang Zhao, Yan Zhu, Sabine J. Seidel. The chaos in calibrating crop models. . 2020; ():1.
Chicago/Turabian StyleDaniel Wallach; Taru Palosuo; Peter Thorburn; Zvi Hochman; Emmanuelle Gourdain; Fety Andrianasolo; Senthold Asseng; Bruno Basso; Samuel Buis; Neil Crout; Camilla Dibari; Benjamin Dumont; Roberto Ferrise; Thomas Gaiser; Cecile Garcia; Sebastian Gayler; Afshin Ghahramani; Santosh Hiremath; Steven Hoek; Heidi Horan; Gerrit Hoogenboom; Mingxia Huang; Mohamed Jabloun; Per-Erik Jansson; Qi Jing; Eric Justes; Kurt Christian Kersebaum; Anne Klosterhalfen; Marie Launay; Elisabet Lewan; Qunying Luo; Bernardo Maestrini; Henrike Mielenz; Marco Moriondo; Hasti Nariman Zadeh; Gloria Padovan; Jørgen Eivind Olesen; Arne Poyda; Eckart Priesack; Johannes Wilhelmus Maria Pullens; Budong Qian; Niels Schuetze; Vakhtang Shelia; Amir Souissi; Xenia Specka; Amit Kumar Srivastava; Tommaso Stella; Thilo Streck; Giacomo Trombi; Evelyn Wallor; Jing Wang; Tobias K.D. Weber; Lutz Weihermueller; Allard de Wit; Thomas Woehling; Liujun Xiao; Chuang Zhao; Yan Zhu; Sabine J. Seidel. 2020. "The chaos in calibrating crop models." , no. : 1.
Heat stress is a significant challenge in dairy farming systems. Dairy cows under heat stress will encounter impaired welfare leading to production losses. As the frequency and magnitude of heat stress events increase in the coming decades, a focus on heat stress reduction studies becomes important. Modelling and on-farm experiments have been used to assess the effects of heat stress on livestock over the last few decades. Mitigation solutions including optimal shed structure, ventilation, feeding regimes, farm management and genetic selection have all been explored. However, under different farm conditions, the heat tolerance and coping ability of dairy cows can vary significantly. Until now, the results from different mathematical models have provided a variety of heat stress thresholds for on-farm use. In practice, it is still costly to determine an accurate heat stress level in order to identify the mitigation requirements. This review summarises previous studies on the effects of heat stress on intensively reared dairy cows and different mitigation approaches. We have undertaken a comparative analysis of thermal indices, animal responses, and mitigation approaches. Recommendations are then given for developing a framework to enhance the measurement, assessment and mitigation of heat stress. Robust monitoring systems, big data analyses and artificial intelligence algorithms are needed for the future development of dynamic, self-calibrating model-based systems, which could provide real-time assessment and minimisation of heat stress.
Boyu Ji; Thomas Banhazi; Kristen Perano; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. A review of measuring, assessing and mitigating heat stress in dairy cattle. Biosystems Engineering 2020, 199, 4 -26.
AMA StyleBoyu Ji, Thomas Banhazi, Kristen Perano, Afshin Ghahramani, Les Bowtell, Chaoyuan Wang, Baoming Li. A review of measuring, assessing and mitigating heat stress in dairy cattle. Biosystems Engineering. 2020; 199 ():4-26.
Chicago/Turabian StyleBoyu Ji; Thomas Banhazi; Kristen Perano; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. 2020. "A review of measuring, assessing and mitigating heat stress in dairy cattle." Biosystems Engineering 199, no. : 4-26.
Heat stress is usually assessed using thermal comfort indices (TCIs) that calculate integrated values of temperature, humidity, wind speed and solar radiation. However, the negative effect of heat stress is related not only to the intensity but also the duration of heat stress endured, as well as the accumulated influence from previous time periods. This study was conducted to develop adjusted TCIs which could simultaneously quantify the intensity and duration of heat stress. The data for this study came from production and climate data collected on a robotically milked dairy-farm. Thresholds of heat stress under diurnal pattern (0–24 h) and lag pattern (−90 to 0 d) for different TCIs were identified to be related to a significant decrease of daily milk yields (DMY). An intensity duration index (IDI) was proposed to evaluate daily short-term heat stress (HIDI) and heat stress relief (RIDI), by multiplying the percentage difference between TCIs and their threshold values (i.e. intensity) by the duration. Thresholds of HIDI and RIDI, as well as their sum i.e. IDItotal were identified to describe multiple levels of heat stress with different significant decrease rate of DMY from −0.01 to −0.05 kg∙cow−1∙d−1[DMY]∙(%∙h)−1[IDI]. To evaluate the long-term lag and cumulative effects of heat stress, TCIs with weighted cumulative adjustment (TCIWCE) and heat stress adjustment (TCIHS) were defined by assigning a different level of importance to daily TCIs of the previous period and calculating the average of these weighted TCIs. Multiple linear regressions between DMY and adjusted TCIs (i.e. TCIWCE and TCIHS) were performed considering age, body mass (BM) and days in milk (DIM) as basic independent variables. The adjusted R squared (R2) and residual root mean square (RMS) values of these regressions were used for comparison. Using adjustment TCIWCE or TCIHS was found to increase the adjusted R2 and decrease RSS, indicating an improved explanation of variance in heat stress impact.
Boyu Ji; Thomas Banhazi; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. Modelling of heat stress in a robotic dairy farm. Part 4: Time constant and cumulative effects of heat stress. Biosystems Engineering 2020, 199, 73 -82.
AMA StyleBoyu Ji, Thomas Banhazi, Afshin Ghahramani, Les Bowtell, Chaoyuan Wang, Baoming Li. Modelling of heat stress in a robotic dairy farm. Part 4: Time constant and cumulative effects of heat stress. Biosystems Engineering. 2020; 199 ():73-82.
Chicago/Turabian StyleBoyu Ji; Thomas Banhazi; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. 2020. "Modelling of heat stress in a robotic dairy farm. Part 4: Time constant and cumulative effects of heat stress." Biosystems Engineering 199, no. : 73-82.
Study region: North Johnstone catchment, located in the north east of Australia. The catchment has wet tropical climate conditions and is one of the major sediment contributors to the Great Barrier Reef. Study focus: The purpose of this paper was to identify soil erosion hotspots through simulating hydrological processes, soil erosion and sediment transport using the Soil and Water Assessment Tool (SWAT). In particular, we focused on predictive uncertainty in the model evaluations and presentations—a major knowledge gap for hydrology and soil erosion modelling in the context of Great Barrier Reef catchments. We carried out calibration and validation along with uncertainty analysis for streamflow and sediment at catchment and sub-catchment scales and investigated details of water balance components, the impact of slope steepness and spatio-temporal variations on soil erosion. The model performance in simulating actual evapotranspiration was compared with those of the Australian Landscape Water Balance (AWRA-L) model to increase our confidence in simulating water balance components. New hydrological insights for the region: The spatial locations of soil erosion hotspots were identified and their responses to different climatic conditions were quantified. Furthermore, a set of land use scenarios were designed to evaluate the effect of reforestation on sediment transport. We anticipate that protecting high steep slopes areas, which cover a relatively small proportion of the catchment (4–9%), can annually reduce 15–26% sediment loads to the Great Barrier Reef.
Vahid Rafiei; Afshin Ghahramani; Duc-Anh An-Vo; Shahbaz Mushtaq. Modelling Hydrological Processes and Identifying Soil Erosion Sources in a Tropical Catchment of the Great Barrier Reef Using SWAT. Water 2020, 12, 2179 .
AMA StyleVahid Rafiei, Afshin Ghahramani, Duc-Anh An-Vo, Shahbaz Mushtaq. Modelling Hydrological Processes and Identifying Soil Erosion Sources in a Tropical Catchment of the Great Barrier Reef Using SWAT. Water. 2020; 12 (8):2179.
Chicago/Turabian StyleVahid Rafiei; Afshin Ghahramani; Duc-Anh An-Vo; Shahbaz Mushtaq. 2020. "Modelling Hydrological Processes and Identifying Soil Erosion Sources in a Tropical Catchment of the Great Barrier Reef Using SWAT." Water 12, no. 8: 2179.
Australian and Queensland Government's Reef 2050 Water Quality Improvement Plan has set targets for improving the water quality entering the Great Barrier Reef lagoon. Given the large public investment and the deficit of data linking on-farm land management to changes in environmental outcomes, there is a need for a robust and efficient methods of quantifying links between land management and water quality. This paper explores a pragmatic approach to making this link using available data. We demonstrate that a simple parameterisation process is suitable for estimating hydrology and water quality across a wide range of land uses and management practices in agricultural landscapes. However, a manually calibrated model may still require the analysis of parameters to reduce error variances and evaluate uncertainties. Confidence in estimating hydrology and water quality in descending order is: runoff, sediment, nitrogen, phosphorous, and pesticide losses, reflecting the availability of data and inherent error propagation.
Afshin Ghahramani; David M. Freebairn; Dipaka R. Sena; Justin L. Cutajar; David M. Silburn. A pragmatic parameterisation and calibration approach to model hydrology and water quality of agricultural landscapes and catchments. Environmental Modelling & Software 2020, 130, 104733 .
AMA StyleAfshin Ghahramani, David M. Freebairn, Dipaka R. Sena, Justin L. Cutajar, David M. Silburn. A pragmatic parameterisation and calibration approach to model hydrology and water quality of agricultural landscapes and catchments. Environmental Modelling & Software. 2020; 130 ():104733.
Chicago/Turabian StyleAfshin Ghahramani; David M. Freebairn; Dipaka R. Sena; Justin L. Cutajar; David M. Silburn. 2020. "A pragmatic parameterisation and calibration approach to model hydrology and water quality of agricultural landscapes and catchments." Environmental Modelling & Software 130, no. : 104733.
Robotic milking systems (RMS) have been demonstrated to reduce on-farm labour requirements and collect significant individual-level data in relation to animal health, welfare and production, but they are still largely underutilised. Studies on the relationship between heat stress, animal behaviour and robotic milking performance in a RMS are still insufficient. To model such a relationship, this study focused on analysing the data collected from a RMS system. Animal response indicators of heat stress assessment were rumination time (RT), milk temperature (MT) and daily milk yield (DMY). In addition, RMS milking behaviour, i.e. time of milking (TM), milking frequency (MF), milking duration (MD), milking speed (MS) and milk yield per milking (MY) were also monitored. A new index of rumination efficiency (REI) was created to evaluate rumination efficiency under heat stress, defined as the ratio between DMY and RT. Using multiple broken-line regression, it was found that a 1 °C rise in daily mean temperature could reduce RT by 5.12 min, decrease REI by 0.07 kg·cow−1·h−1, and increase low efficiency milking by 1%. Moreover, the study also found cows prefer to milk between 7:00–9:00AM, and 86% of milking events happened during this period. No significant correlation was found between heat stress and milking behaviour. However, delaying the first milking event of the day and controlling milking intervals to <4 h, was beneficial for REI and robotic milking performance.
Boyu Ji; Thomas Banhazi; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. Modelling of heat stress in a robotic dairy farm. Part 3: Rumination and milking performance. Biosystems Engineering 2020, 199, 58 -72.
AMA StyleBoyu Ji, Thomas Banhazi, Afshin Ghahramani, Les Bowtell, Chaoyuan Wang, Baoming Li. Modelling of heat stress in a robotic dairy farm. Part 3: Rumination and milking performance. Biosystems Engineering. 2020; 199 ():58-72.
Chicago/Turabian StyleBoyu Ji; Thomas Banhazi; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. 2020. "Modelling of heat stress in a robotic dairy farm. Part 3: Rumination and milking performance." Biosystems Engineering 199, no. : 58-72.
Mixed crop-livestock farming systems provide food for over half of the global population. However, some important food exporting countries, like Australia, are predicted to be vulnerable to climate change and may require transformative adaptations if they are to continue their role in food exportation. This paper assesses the potential impacts of projected climate change by 2030 (0.4–1.6° increase in mean temperature) on Australian mixed crop-livestock systems and examines the consequences of shifts in land allocations to cropping and grazing, in these systems, as an adaptation option. Farm bio-economic simulation models were developed for these mixed enterprise systems in several regions of Australia. These models were based on biophysically coupled crop, pasture, and livestock simulation models that in turn drew on site-based downscaled climate projection datasets. The farm models calculated farm profitability and risk measures. A range of land use changes was investigated. At drier locations facing adverse climate change, results showed a transition towards a greater emphasis on livestock production could be beneficial when assessed against multiple criteria of farm profit, downside financial risk, and environmental damage. We highlight some industry and government actions and policies that could facilitate these preferred adaptation strategies at such locations.
Afshin Ghahramani; Ross Kingwell; Tek Narayan Maraseni. Land use change in Australian mixed crop-livestock systems as a transformative climate change adaptation. Agricultural Systems 2020, 180, 102791 .
AMA StyleAfshin Ghahramani, Ross Kingwell, Tek Narayan Maraseni. Land use change in Australian mixed crop-livestock systems as a transformative climate change adaptation. Agricultural Systems. 2020; 180 ():102791.
Chicago/Turabian StyleAfshin Ghahramani; Ross Kingwell; Tek Narayan Maraseni. 2020. "Land use change in Australian mixed crop-livestock systems as a transformative climate change adaptation." Agricultural Systems 180, no. : 102791.
Managed temperate grasslands occupy 25% of the world, which is 70% of global agricultural land. These lands are an important source of food for the global population. This review paper examines the impacts of climate change on managed temperate grasslands and grassland-based livestock and effectiveness of adaptation and mitigation options and their interactions. The paper clarifies that moderately elevated atmospheric CO2 (eCO2) enhances photosynthesis, however it may be restiricted by variations in rainfall and temperature, shifts in plant’s growing seasons, and nutrient availability. Different responses of plant functional types and their photosynthetic pathways to the combined effects of climatic change may result in compositional changes in plant communities, while more research is required to clarify the specific responses. We have also considered how other interacting factors, such as a progressive nitrogen limitation (PNL) of soils under eCO2, may affect interactions of the animal and the environment and the associated production. In addition to observed and modelled declines in grasslands productivity, changes in forage quality are expected. The health and productivity of grassland-based livestock are expected to decline through direct and indirect effects from climate change. Livestock enterprises are also significant cause of increased global greenhouse gas (GHG) emissions (about 14.5%), so climate risk-management is partly to develop and apply effective mitigation measures. Overall, our finding indicates complex impact that will vary by region, with more negative than positive impacts. This means that both wins and losses for grassland managers can be expected in different circumstances, thus the analysis of climate change impact required with potential adaptations and mitigation strategies to be developed at local and regional levels.
Afshin Ghahramani; S. Mark Howden; Agustin Del Prado; Dean T. Thomas; Andrew D. Moore; Boyu Ji; Serkan Ates. Climate Change Impact, Adaptation, and Mitigation in Temperate Grazing Systems: A Review. Sustainability 2019, 11, 7224 .
AMA StyleAfshin Ghahramani, S. Mark Howden, Agustin Del Prado, Dean T. Thomas, Andrew D. Moore, Boyu Ji, Serkan Ates. Climate Change Impact, Adaptation, and Mitigation in Temperate Grazing Systems: A Review. Sustainability. 2019; 11 (24):7224.
Chicago/Turabian StyleAfshin Ghahramani; S. Mark Howden; Agustin Del Prado; Dean T. Thomas; Andrew D. Moore; Boyu Ji; Serkan Ates. 2019. "Climate Change Impact, Adaptation, and Mitigation in Temperate Grazing Systems: A Review." Sustainability 11, no. 24: 7224.
Thresholds of heat stress are identified by determining the values of thermal comfort indices with significant change of animal responses. However, published thresholds may lead to inaccuracy when dealing with specific climate conditions, animal breeds and production factors. Thus, determining dynamic thresholds might provide better assessment of heat stress, with self-calibration capabilities. In this study, a large dataset of individual age, body mass (BM), days in milk (DIM), daily milk yield (DMY) and milk temperature (MT) of 126 lactating Holstein cows was collected from a robotic dairy farm over five years. The ambient temperature data was collected from a local weather station and processed as daily minimum and mean temperature (Tmin and Tmean). For the whole herd, a new series of heat stress thresholds with stages were defined as comfort stage, milk heat stress, effective heat stress and critical heat stress. The definition was based on the cow's responses in DMY and MT, which provides a potential approach to accurately alert for heat stress in robotic farming systems by using the existing data source. For the specific individuals, dynamic thresholds of heat stress were identified and categorised using the decision tree machine learning model. The categorisation achieved 79–94% overall accuracy, and demonstrated the importance of cooling cows during their early lactation period.
Boyu Ji; Thomas Banhazi; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. Modelling of heat stress in a robotic dairy farm. Part 2: Identifying the specific thresholds with production factors. Biosystems Engineering 2019, 199, 43 -57.
AMA StyleBoyu Ji, Thomas Banhazi, Afshin Ghahramani, Les Bowtell, Chaoyuan Wang, Baoming Li. Modelling of heat stress in a robotic dairy farm. Part 2: Identifying the specific thresholds with production factors. Biosystems Engineering. 2019; 199 ():43-57.
Chicago/Turabian StyleBoyu Ji; Thomas Banhazi; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. 2019. "Modelling of heat stress in a robotic dairy farm. Part 2: Identifying the specific thresholds with production factors." Biosystems Engineering 199, no. : 43-57.
Thermal comfort indices (TCIs) have been developed to assess heat stress and model the relationship between thermal parameters (e.g. dry-bulb temperature) and animal responses (e.g. daily milk yield, DMY). The published models typically include temperature humidity index (THI), black globe humidity index, environmental stress index, equivalent temperature index, heat load index, respiration rate index and comprehensive climate index. This study was conducted to compare the performance of these published TCIs using data collected from a robotic farm situated in a subtropical climate region. The comparison also included models formulated between basic thermal parameters and animal responses (DMY and milk temperature (MT)). The statistical analyses found dry-bulb temperature can provide a similar level of performance to other TCIs in assessing heat stress. The spatial variability between on-farm measurements and the local weather station can be neglected when modelling with TCIs and MT. For cows with an average DMY of 31 kg cow−1 d−1, the threshold for significant decline of DMY was reported as THI >64 (P < 0.05). The daily minimum TCIs were found to be highly correlated with production loss, indicating that sufficient night-time cooling was important for preventing production losses. The potential of implementing a simplified assessment of heat stress using an on-line dataset was also demonstrated by this study.
Boyu Ji; Thomas Banhazi; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. Modelling of heat stress in a robotic dairy farm. Part 1: Thermal comfort indices as the indicators of production loss. Biosystems Engineering 2019, 199, 27 -42.
AMA StyleBoyu Ji, Thomas Banhazi, Afshin Ghahramani, Les Bowtell, Chaoyuan Wang, Baoming Li. Modelling of heat stress in a robotic dairy farm. Part 1: Thermal comfort indices as the indicators of production loss. Biosystems Engineering. 2019; 199 ():27-42.
Chicago/Turabian StyleBoyu Ji; Thomas Banhazi; Afshin Ghahramani; Les Bowtell; Chaoyuan Wang; Baoming Li. 2019. "Modelling of heat stress in a robotic dairy farm. Part 1: Thermal comfort indices as the indicators of production loss." Biosystems Engineering 199, no. : 27-42.
Monitoring and managing floods and sediments are considered major challenges in the sustainable management of watersheds. Hence, an effective layout of the hydraulic structures across channels is considered as a measure to overcome these two challenges. This study was carried out in the Illanlu catchment in the northwest of Hamadan Province, Iran. A set of effective indices were identified and developed using the Geographic Information System (GIS) and fuzzy logic to model optimal location of sites for construction of the check dams. Based on the ability to construct the check dams e.g. access road and environmental constraints, the map was classified from very high to very low construction ability. In the study area, 26% of the catchment area was located in the very high and high classes, predominantly in the upstream sub-catchments, 13% in the very low class, and 62% in moderate and low ability ranges. Finally, by using superimposing method and the receiver operating characteristic (ROC) curve the accuracy of developed spatial model estimated to be 70% and 73.5%, respectively.
Ali Reza Ildoromi; Mehdi Sepehri; Hossein Malekinezhad; Mahboobeh Kiani-Harchegani; Afshin Ghahramani; Seyed Zeynalabedin Hosseini; Mohammad Mehdi Artimani. Application of multi-criteria decision making and GIS for check dam layout in the Ilanlu basin, northwest of Hamadan Province, Iran. Physics and Chemistry of the Earth, Parts A/B/C 2019, 114, 102803 .
AMA StyleAli Reza Ildoromi, Mehdi Sepehri, Hossein Malekinezhad, Mahboobeh Kiani-Harchegani, Afshin Ghahramani, Seyed Zeynalabedin Hosseini, Mohammad Mehdi Artimani. Application of multi-criteria decision making and GIS for check dam layout in the Ilanlu basin, northwest of Hamadan Province, Iran. Physics and Chemistry of the Earth, Parts A/B/C. 2019; 114 ():102803.
Chicago/Turabian StyleAli Reza Ildoromi; Mehdi Sepehri; Hossein Malekinezhad; Mahboobeh Kiani-Harchegani; Afshin Ghahramani; Seyed Zeynalabedin Hosseini; Mohammad Mehdi Artimani. 2019. "Application of multi-criteria decision making and GIS for check dam layout in the Ilanlu basin, northwest of Hamadan Province, Iran." Physics and Chemistry of the Earth, Parts A/B/C 114, no. : 102803.
This study aimed to examine flood hazard zoning and assess the role of check dams as effective hydraulic structures in reducing flood hazards. To this end, factors associated with topographic, hydrologic and human characteristics were used to develop indices for flood mapping and assessment. These indices and their components were weighed for flood hazard zoning using two methods: (i) a multi-criterion decision-making model in fuzzy logic and (ii) entropy weight. After preparing the flood hazard map by using the above indices and methods, the characteristics of the change‐point were used to assess the role of the check dams in reducing flood risk. The method was used in the Ilanlu catchment, located in the northwest of Hamadan province, Iran, where it is prone to frequent flood events. The results showed that the area of ‘very low’, ‘low’ and ‘moderate’ flood hazard zones increased from about 2.2% to 7.3%, 8.6% to 19.6% and 22.7% to 31.2% after the construction of check dams, respectively. Moreover, the area of ‘high’ and ‘very high’ flood hazard zones decreased from 39.8% to 29.6%, and 26.7% to 12.2%, respectively.
Mehdi Sepehri; Ali Reza Ildoromi; Hossein Malekinezhad; Afshin Ghahramani; Mohammad Reza Ekhtesasi; Chen Cao; Mahboobeh Kiani-Harchegani. Assessment of check dams’ role in flood hazard mapping in a semi-arid environment. Geomatics, Natural Hazards and Risk 2019, 10, 2239 -2256.
AMA StyleMehdi Sepehri, Ali Reza Ildoromi, Hossein Malekinezhad, Afshin Ghahramani, Mohammad Reza Ekhtesasi, Chen Cao, Mahboobeh Kiani-Harchegani. Assessment of check dams’ role in flood hazard mapping in a semi-arid environment. Geomatics, Natural Hazards and Risk. 2019; 10 (1):2239-2256.
Chicago/Turabian StyleMehdi Sepehri; Ali Reza Ildoromi; Hossein Malekinezhad; Afshin Ghahramani; Mohammad Reza Ekhtesasi; Chen Cao; Mahboobeh Kiani-Harchegani. 2019. "Assessment of check dams’ role in flood hazard mapping in a semi-arid environment." Geomatics, Natural Hazards and Risk 10, no. 1: 2239-2256.
Rainfall is low and unreliable in much of Australia's dryland cropping areas, requiring well-informed crop management for optimising yield and profit. Growing-season rainfall is usually supplemented by soil water during fallow periods preceding a crop. While rainfall is conveniently measured, the difficulty of measuring a soil's plant available water (PAW, mm) has led to using simulation models for estimating PAW. Here we developed a smartphone application (app) that simulates soil water balance by accessing weather, soil and crop data from databases and on-farm records. Predictions of PAW using the Howleaky modelling engine were compared with field measurements. Validation of the simulation engine across sites in Australian cropping areas showed good agreement between simulated and measured PAW. Errors in model estimates are compared with variability found within small fields. We conclude that estimating PAW for paddocks using a simulation model built in a smartphone app is a reliable and adaptable technology.
D.M. Freebairn; Afshin Ghahramani; J.B. Robinson; D.J. McClymont. A tool for monitoring soil water using modelling, on-farm data, and mobile technology. Environmental Modelling & Software 2018, 104, 55 -63.
AMA StyleD.M. Freebairn, Afshin Ghahramani, J.B. Robinson, D.J. McClymont. A tool for monitoring soil water using modelling, on-farm data, and mobile technology. Environmental Modelling & Software. 2018; 104 ():55-63.
Chicago/Turabian StyleD.M. Freebairn; Afshin Ghahramani; J.B. Robinson; D.J. McClymont. 2018. "A tool for monitoring soil water using modelling, on-farm data, and mobile technology." Environmental Modelling & Software 104, no. : 55-63.
Mixed crop-livestock farming systems provide food for more than half of the world's population. These agricultural systems are predicted to be vulnerable to climate change and therefore require transformative adaptations. In collaboration with farmers in the wheatbelt of Western Australia (WA), a range of systemic and transformative adaptation options, e.g. land use change, were designed for the modelled climate change projected to occur in 2030 (0.4–1.4° increase in mean temperature). The effectiveness of the adaptation options was evaluated using coupled crop and livestock biophysical models within an economic and environmental framework at both the enterprise and farm scales. The relative changes in economic return and environmental variables in 2030 are presented in comparison with a baseline period (1970–2010). The analysis was performed on representative farm systems across a rainfall transect. Under the impact of projected climate change, the economic returns of the current farms without adaptation declined by between 2 and 47%, with a few exceptions where profit increased by up to 4%. When the adaptations were applied for 2030, profit increased at the high rainfall site in the range between 78 and 81% through a 25% increase in the size of livestock enterprise and adjustment in sowing dates, but such profit increases were associated with 6–10% increase in greenhouse gas (GHG) emissions. At the medium rainfall site, a 100% increase in stocking rate resulted in 5% growth in profit but with a 61–71% increase in GHG emissions and the increased likelihood of soil degradation. At the relatively low rainfall site, a 75% increase in livestock when associated with changes in crop management resulted in greater profitability and a smaller risk of soil erosion. This research identified that a shift toward a greater livestock enterprises (stocking rate and pasture area) could be a profitable and low-risk approach and may have most relevance in years with extremely low rainfall. If transformative adaptations are adopted then there will be an increased requirement for an emissions control policy due to livestock GHG emissions, while there would be also need for soil conservation strategies to be implemented during dry periods. The adoption rate analysis with producers suggests there would be a greater adoption rate for less intensified adaptations even if they are transformative. Overall the current systems would be more resilient with the adaptations, but there may be challenges in terms of environmental sustainability and in particular with soil conservation.
Afshin Ghahramani; David Bowran. Transformative and systemic climate change adaptations in mixed crop-livestock farming systems. Agricultural Systems 2018, 164, 236 -251.
AMA StyleAfshin Ghahramani, David Bowran. Transformative and systemic climate change adaptations in mixed crop-livestock farming systems. Agricultural Systems. 2018; 164 ():236-251.
Chicago/Turabian StyleAfshin Ghahramani; David Bowran. 2018. "Transformative and systemic climate change adaptations in mixed crop-livestock farming systems." Agricultural Systems 164, no. : 236-251.
The state of Western Australia is a major producer and exporter of crops and livestock. Mixed farming systems are typical agricultural enterprises in the Western Australian wheatbelt where climate drives the productivity and profitability of these farms and therefore the effects of likely climate change on their performance need to be understood. Here the effects of climate change projected at 2030 were evaluated compared to a baseline period (1980–1999) on mixed farming systems at paddock, enterprise and whole farm scales using the coupled APSIM and GRAZPLAN biophysical simulation models. The yield of different crops, livestock production and gross margins were assessed under current and projected climates using current farming technology and management practices. Representative mixed-farm systems were selected along a climate transect. Modelling analysis suggests that current production levels and gross margins of mixed farm systems in Western Australia will not be sustained in 2030 climate conditions except in areas of moderately high-rainfall. Whole farm gross margin declined at all site × potential climate scenarios between 1% and 22% except in moderately high rainfall where gross margin increased by up to 4% under a ‘hot and moderate change in rainfall’ climate. Projected crop yields declined for most of the crop × site × potential climate combinations, with greatest declines under a hot and dry climate (at driest margin of transect) in which wheat, barley, canola, and lupin yield declined up to 16%, 15%, 21%, and 27%, respectively. Increase in yield was predicted for wheat and barley at some of the site × potential climate s. Wheat yield increased only under moderately high rainfall region by 6% while barley increased by 1%. Simulated cropping gross margin was also shown to decline by between > 1% and 23%, except for the moderately high-rainfall site where cropping gross margins were projected to increase by up to 3%. Changes in simulated livestock production were smaller and less variable than for crop production. The change in weight of livestock sold across sites × potential climate combinations ranged between − 3% and + 3%. Livestock gross margin varied between − 11% and + 6%. Modelling results indicated a greater fertilisation effect of the elevated CO2 on pasture production than on crop yield and biomass particularly in drier sites. But however, this could not offset negative impact of climate change under hot potential climates. The main negative environmental impacts from the projected climate change were declines in annual net primary production (ANPP), ground cover and water use efficiency mostly at drier sites. Whole farm N2O emission declined significantly for the majority of site × potential climate combinations, while smaller decreases in ruminant CH4 emission were predicted. In 2030, returns from livestock enterprises are predicted to be smaller, but less variable than from cropping and with increasing probability of success in drier regions. Reduced variability in financial return is important from the perspective of whole farm risk management. Shifts in enterprise mix in dryland mixed-farming systems towards increased livestock may be a helpful strategy in adapting to climate change and managing the associated financial risks.
Afshin Ghahramani; Andrew Moore. Impact of climate changes on existing crop-livestock farming systems. Agricultural Systems 2016, 146, 142 -155.
AMA StyleAfshin Ghahramani, Andrew Moore. Impact of climate changes on existing crop-livestock farming systems. Agricultural Systems. 2016; 146 ():142-155.
Chicago/Turabian StyleAfshin Ghahramani; Andrew Moore. 2016. "Impact of climate changes on existing crop-livestock farming systems." Agricultural Systems 146, no. : 142-155.
Greenhouse gas emissions (GHG) from broadacre sheep farms constitute ~16% of Australia’s total livestock emissions. To study the diversity of Australian sheep farming enterprises a combination of modelling packages was used to calculate GHG emissions from three sheep enterprises (Merino ewe production for wool and meat, Merino-cross ewes with an emphasis on lamb production, and Merino wethers for fine wool production) at 28 sites across eight climate zones in southern Australia. GHG emissions per ha, per dry sheep equivalents and emissions intensity (EI) per tonne of clean wool or liveweight sold under different pasture management or animal breeding options (that had been previously determined in interviews with farmers) were assessed relative to baseline farms in each zone (‘Nil’ option). Increasing soil phosphorus fertility or sowing 40% of the farm area to lucerne resulted in the smallest and largest changes in GHG/dry sheep equivalents, respectively (–66%, 113%), though both of these options had little influence on EI for either clean wool or liveweight sold. Breeding ewes with greater body size or genotypes with higher fleece weight resulted in 11% and 9% reductions, respectively, in EI. Enterprises specialising in lamb production (crossbred ewes) had 89% lower EI than enterprises specialising in fine wool production (Merino wethers). Thus, sheep producers aiming for lower EI could focus more on liveweight turnoff than wool production. Emissions intensities were typically highest in cool temperate regions with high rainfall and lowest in semiarid and arid regions with low aboveground net primary productivity. Overall, animal breeding options reduced EI more than feedbase interventions.
D. J. Cottle; M. T. Harrison; A. Ghahramani. Sheep greenhouse gas emission intensities under different management practices, climate zones and enterprise types. Animal Production Science 2016, 56, 507 -518.
AMA StyleD. J. Cottle, M. T. Harrison, A. Ghahramani. Sheep greenhouse gas emission intensities under different management practices, climate zones and enterprise types. Animal Production Science. 2016; 56 (3):507-518.
Chicago/Turabian StyleD. J. Cottle; M. T. Harrison; A. Ghahramani. 2016. "Sheep greenhouse gas emission intensities under different management practices, climate zones and enterprise types." Animal Production Science 56, no. 3: 507-518.