Management options for dairy farms under climate change: Effects of intensification, adaptation and simplification on pastures, milk production and profitability

Harrison, Matthew T.; Cullen, Brendan; Armstrong, Dan P.

doi:10.1016/j.agsy.2017.04.003

Cited by 63 publications

(45 citation statements)

References 39 publications

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“…Almost all dairy farmers in Pakistan exceptionally observe climate change and its variability. This reality advocates that farmers’ knowledge of climate change and their experience of extreme weather events can improve their perception [71,72].…”

Section: Discussionmentioning

confidence: 99%

Dairy Production under Climatic Risks: Perception, Perceived Impacts and Adaptations in Punjab, Pakistan

Abbas

Han

Adeel

et al. 2019

IJERPH

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The changing climatic conditions coupled with fodder availability have posed severe challenges and threats for the dairy sector in Pakistan. The current paper determines the influence of climate change on the dairy sector in Pakistan. Comprehensive data set was collected from 450 farmers. The majority of farmers experienced the climate change and its variability and explained that severity and frequency of climatic extreme events such as droughts, heat waves, floods, pests and diseases and humidity is increasing. The study found that farmers considered drought as one of the major climatic risks which severely affects all aspects of dairy production. Specifically, to estimate the perceived impacts of climatic extreme event on milk production, an ordered probit model was applied and identified that climate change had high adverse impact on milk quantity in the study area. Different adaptation practices, such as changing cropping pattern for fodder production, off-farm income activities, diversifying the farm and regular vaccination are mostly used by dairy farmers. The study recommends policy initiatives to be taken by government for long term developments in the dairy farming.

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Section: Discussionmentioning

confidence: 99%

Dairy Production under Climatic Risks: Perception, Perceived Impacts and Adaptations in Punjab, Pakistan

Abbas

Han

Adeel

et al. 2019

IJERPH

View full text Add to dashboard Cite

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“…These include source imagery being expensive to acquire, the lack of spatial coverage, a low revisit time, difficulties during data processing and, therefore, the technology being impractical for fit-for-purpose applications. Grazing in intensive livestock systems, such as dairy production, is done with high stocking rates and rapid rotations [9][10][11]. The goal is to remove most of the standing biomass rapidly and uniformly without giving the animals the chance to be selective and then leave the pasture without grazing for an extended period to regrow biomass until the next grazing cycle.…”

Section: Introductionmentioning

confidence: 99%

Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning

et al. 2021

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Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.

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“…In field crop experimental trials, waterlogging driven by excessive rainfall or subsurface or lateral flooding may have poor reproducibility, because waterlogging‐prone environments have considerable complexity, including variable dimensions of time, space, biology, and chemistry. Thus, methods with which such events are analyzed and quantified in a farming systems context require careful consideration (Harrison, Cullen, & Armstrong, 2017; Harrison, Cullen, & Rawnsley, 2016).…”

Section: Introductionmentioning

confidence: 99%