Effects of subsurface drip irrigation rates and furrow irrigation for cotton grown on a vertisol on off-site movement of sediments, nutrients and pesticides

McHugh, AD; Bhattarai, Surya P.; Lotz, G.; Midmore, David J.

doi:10.1051/agro:2008034

Cited by 37 publications

(25 citation statements)

References 33 publications

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“…Nitrate and DON were the main components of TN present in the irrigation water, and both N species were lost from the cotton field (Table 1). The measured NO 3 -N concentrations are similar to those from studies within the Australian cotton industry (Mchugh et al 2008;Weaver et al 2013) and other irrigated cropping systems (Harrison et al 2005). Salt and other nutrients accumulate as a result of evaporation from the furrow surface (Noborio et al 1996) and are remobilised during the first flush at the beginning of an irrigation.…”

Section: Electrical Conductivity Ph Dissolved Nitrate and Organic Nsupporting

confidence: 71%

“…Excess N may also be leached (Benjamin et al 1998) or lost as runoff into the irrigation system (Mchugh et al 2008). A study of furrow-irrigated cotton in Emerald, Queensland, Australia, with application of 250 kg N ha , showed average N runoff to be 18.8 and 11.3 kg N ha -1 for 2001-02 and 2002-03, respectively (Mchugh et al 2008).…”

Section: Introductionmentioning

confidence: 99%

“…A study of furrow-irrigated cotton in Emerald, Queensland, Australia, with application of 250 kg N ha , showed average N runoff to be 18.8 and 11.3 kg N ha -1 for 2001-02 and 2002-03, respectively (Mchugh et al 2008). In furrow-irrigated maize production systems in Iran, nitrate (NO 3 -) runoff ranged from 26 to 70 N ha -1 after application of 60 kg N ha -1 (Ebrahimian et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Current estimates from the Intergovernmental Panel on Climate Change (IPCC) suggest that for each kg N lost via runoff or leaching, 0.0025 kg N 2 O-N may be produced (emission factor, EF5g = 0.0025) but the range of uncertainty is large at 0.0005 to 0.025 (IPCC 2006). Based on the NO 3 -N losses reported by Mchugh et al (2008), we might estimate that~0.028-0.047 kg N 2 O-N ha -1 would be produced from furrow-irrigated cotton via indirect emissions. Harrison and Matson (2003) have shown with direct measurement that average emissions of 0.04 N 2 O-N kg ha -1 day -1…”

Section: Introductionmentioning

confidence: 99%

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Contribution of the cotton irrigation network to farm nitrous oxide emissions

et al. 2016

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Nitrous oxide (N2O) is a potent greenhouse gas, and agriculture is the dominant source of N2O-N emissions. The Australian cotton industry requires high inputs of N to maintain high lint quality and yields; however, over-fertilisation with N is symptomatic of the industry. Up to 3.5% of N fertiliser applied is lost directly from cotton fields as N2O gas. Excess N may also be lost via erosion, deep-drainage, leaching and runoff, and may subsequently form indirect N2O emissions. The estimate by the Intergovernmental Panel on Climate Change (IPCC) suggests that 0.0025kg N2O-N is produced indirectly from groundwater and surface drainage for each kg N lost via runoff and leaching, although this estimate carries a large degree of uncertainty. This study is the first to address the lack of indirect N2O emission data from irrigated cotton-farming systems. Indirect emissions were determined from total N concentrations in irrigation runoff by using the IPCC emission factor and from measurements of dissolved N2O during the first four irrigations (October–December 2013). Total indirect N2O emissions from the surface of the irrigation network over 3 months when estimated by the dissolved-N2O method were 0.503±0.339kgha–1. By contrast, N2O emissions estimated by the IPCC methodology were 0.843±0.022kgha–1 irrigation surface area. Over the same period of measurement, direct land-surface emissions were 1.44kgN2O-Nha–1 field. Despite relatively high emissions per surface area, the irrigation network is only a minor component of the total farm area, and indirect emissions from the irrigation system contribute ~2.4–4% of the total N2O emissions and <0.02% of the applied N fertiliser.

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Section: Electrical Conductivity Ph Dissolved Nitrate and Organic Nsupporting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contribution of the cotton irrigation network to farm nitrous oxide emissions

et al. 2016

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“…Tanji et al, 1980;Carter et al, 1993;Koluvek et al, 1993). Drip irrigation has been shown to result in much lower off-field transport of sediment than sprinkler and furrow methods (McHugh et al, 2008), and was introduced to California in the early 1960s. Large scale shifts in agricultural practices toward drip irrigation was contemporary with the decrease in suspended sediment Widespread adoption of drip irrigation for such crops could have the unintended side-effect of reducing the entrainment of agricultural sediments into fluvial systems in these regions, which may have beneficial water quality consequences.…”

Section: Discussionmentioning

confidence: 99%

Conversion to drip irrigated agriculture may offset historic anthropogenic and wildfire contributions to sediment production

Gray

Pasternack

Watson

et al. 2016

Science of The Total Environment

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This study is an investigation into the roles of wildfire and changing agricultural practices in controlling the inter-decadal scale trends of suspended sediment production from semi-arid mountainous rivers. In the test case, a decreasing trend in suspended sediment concentrations was found in the lower Salinas River, California between 1967 and 2011. Event to decadal scale patterns in sediment production in the Salinas River have been found to be largely controlled by antecedent hydrologic conditions. Decreasing suspended sediment concentrations over the last 15 years of the record departed from those expected from climatic/hydrologic forcing. Sediment production from the mountainous headwaters of the central California Coast Ranges is known to be dominated by the interaction of wildfire and large rainfall/runoff events, including the Arroyo Seco, a ~700 km 2 subbasin of the Salinas River. However, the decreasing trend in Salinas River suspended sediment concentrations run contrary to increases in the watershed's effective burn area over time. The sediment source area of the Salinas River is an order of magnitude larger than that of the Arroyo Seco, and includes a more complicated mosaic of land cover and land use. The departure from hydrologic forcings on suspended sediment concentration patterns was found to coincide with a rapid conversion of irrigation practices from sprinkler and furrow to subsurface drip irrigation. Changes in agricultural operations appear to have decreased sediment supply to the Salinas River over the late 20 th to early 21 st centuries, obscuring the influence of wildfire on suspended sediment production.

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Predicting within‐field cotton yields using publicly available datasets and machine learning

2021

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Early detection of within-field yield variability for high-value commodity crops, such as cotton (Gossypium spp.), offers growers potential to improve decision-making, optimize yields, and increase profits. Over recent years, publicly available datasets have become increasingly available and at a resolution where within-field yield prediction is possible. However, the viability of using these datasets with machine learning to predict within-field cotton lint yield at key growth stages are largely unknown. This study was conducted on two cotton fields, located near Mungindi, New South Wales, Australia. Three years of yield data, soil, elevation, rainfall, and Landsat imagery were collected from each field. A total of 12 models were created using: (a) two machine learning algorithms: random forest (RF) and gradient boosting machines (GBM); (b) three growth stages: squaring, flowering, and boll-fill; and(c) two different amounts of variables: all variables and the optimal variables determined by a recursive feature elimination (RFE). Results showed a strong agreement between predicted and observed yields at flowering and boll-fill when more information was available. At flowering and boll-fill, root mean square error (RMSE) ranged between 0.15 and 0.20 t ha −1 and Lin's concordance correlation coefficient (LCCC) ranged between 0.50 and 0.66, with RF providing superior results in most cases. Models created using the optimal variables determined by the RFE provided similar results compared to using all variables, allowing greater model accuracy and resolution with targeted sampling. Overall, these findings indicate significant potential of publicly available datasets to predict within-field cotton yield and guide decisionmaking in-season.

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Effects of subsurface drip irrigation rates and furrow irrigation for cotton grown on a vertisol on off-site movement of sediments, nutrients and pesticides

Cited by 37 publications

References 33 publications

Contribution of the cotton irrigation network to farm nitrous oxide emissions

Contribution of the cotton irrigation network to farm nitrous oxide emissions

Conversion to drip irrigated agriculture may offset historic anthropogenic and wildfire contributions to sediment production

Predicting within‐field cotton yields using publicly available datasets and machine learning

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