An irrigated cotton farm emissions case study in NSW, Australia

Powell, Janine; Welsh, Jon; Eckard, Richard

doi:10.1016/j.agsy.2017.09.005

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Ma , et al [ 49 ] suggested that plastic mulch and drip irrigation in cotton production could significantly decrease N 2 O emissions as well as improve water use efficiency. Powell , et al [ 50 ] found that rotating cotton with pulse crops, instead of wheat, could reduce GHG emissions by 8%. In China, 76% of cotton was cultivated in Xinjiang province in 2019, where the soil conditions such as low soil organic matter, light soil texture, and high soil pH, as well as climate factors of low precipitation and high evapotranspiration, restricted soil N 2 O emissions.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Dynamics of Carbon Footprint of Main Crop Production in China

Fan

Guo

Han

et al. 2022

IJERPH

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As a major agricultural country, the comprehensive accounting of the dynamics and composition of the carbon footprint of major crops in China will provide a decision-making basis for environmental management and agricultural green development in the whole process of the major crop production system in China. To investigate the spatiotemporal dynamics of the carbon footprint for major crops in China, a life cycle-based carbon footprint approach was used to evaluate the carbon footprint per unit area (CFA) and per unit yield (CFY) of eight crops for the period of 1990 to 2019. Our results showed that the CFA for all major crops showed an increasing trend with time before 2016 but slowly decreased afterward, while the CFY decreased by 16–43% over the past 30 years due to the increase in crop yield. The three main grain crops, rice (4871 ± 418 kg CO2-eq·ha−1), wheat (2766 ± 552 kg CO2-eq·ha−1), and maize (2439 ± 530 kg CO2-eq·ha−1), showed the highest carbon footprint and contribution to the total greenhouse gas (GHG) emissions, mainly due to their larger cultivated areas and higher fertilizer application rates. CH4 emission was the major component of the carbon footprint for rice production, accounting for 66% and 48% of the CFA and CFY, respectively, while fertilizer production and usage were the largest components of carbon footprint for dryland crops, making up to 26–49% of the CFA and 26–50% of the CFY for different crops. The present study also highlighted the spatial and temporal patterns of the carbon footprint for major crops in China, which could serve as references for the development of best management practices for different crop production in China, to mitigate agricultural GHG emission and to pursue low-carbon agriculture.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Dynamics of Carbon Footprint of Main Crop Production in China

Fan

Guo

Han

et al. 2022

IJERPH

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“…Several examples can be noted. Powell and Scott (2011) described whole-farm risk profiles for a representative mixed farm of wheat, grain legumes, grazing cattle, and 100 ha of irrigated cotton in the lower Namoi Valley, NSW. Luo et al (2017) reported a bioeconomic simulation study on the economic risks of adaptation options in the Australian cotton industry, given the temperature and rainfall regimes projected with climate change by 2030.…”

Section: Introductionmentioning

confidence: 99%

Untangling the complex mix of agronomic and economic uncertainties inherent in decisions on rainfed cotton

Godfrey

Nordblom

Anwar

et al. 2023

Crop & Pasture Science

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Context. Production of rainfed (dryland) cotton (Gossypium hirsutum L.) occurs in many places globally, and is always burdened with greater uncertainties in outcomes than irrigated cotton. Assessing farm financial viability helps farmers to make clearer and more informed decisions with a fuller awareness of the potential risks to their business. Aim. We aimed to highlight key points of uncertainty common in rainfed cotton production and quantify these variable conditions to facilitate clearer decision-making on sowing dates and row configurations. Methods. The consequences of these decisions at six locations across two states in Australia, given estimates of plant-available water at sowing, are expressed in terms of comparable probability distributions of cotton lint yield (derived from crop modelling using historical weather data) and gross margin per hectare (derived from historical prices for inputs and cotton lint yield), using the copula approach. Examples of contrasting conditions and likely outcomes are summarised. Key results. Sowing at the end of October with solid row configuration tended to provide the highest yield; however, single-and double-skip row configurations generally resulted in higher gross margins. Places associated with higher summer-dominant rainfall had greater chance of positive gross margins. Conclusion. In order to maximise the probability of growing a profitable crop, farmers need to consider the variabilities and dependencies within and across price and yield before selecting the most appropriate agronomic decisions. Implications. Given appropriate data on growing conditions and responses, our methodology can be applied in other locations around the world, and to other crops.

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