Economic perspectives on nitrogen in farming systems: managing trade-offs between production, risk and the environment

Pannell, David J.

doi:10.1071/sr16284

Cited by 46 publications

(34 citation statements)

References 50 publications

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“…Nitrogen (N) fertilizer inputs are crucial for achieving high crop yields, but the loss of reactive N from agricultural systems leads to atmospheric, surface water, and groundwater pollution [1][2][3], ultimately diminishing environmental quality and human well-being [4,5]. Despite the potential environmental consequences to society, the pressure on producers to increase productivity oftentimes leads to N fertilizer applications in excess of crop requirement [6,7]. Without more efficient N fertilizer applications, the increasing global population and subsequent rising demand for food are expected to cause an increase in the loss of reactive N in the future [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

et al. 2020

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The ability to predict spatially explicit nitrogen uptake (NUP) in maize (Zea mays L.) during the early development stages provides clear value for making in-season nitrogen fertilizer applications that can improve NUP efficiency and reduce the risk of nitrogen loss to the environment. Aerial hyperspectral imaging is an attractive agronomic research tool for its ability to capture spectral data over relatively large areas, enabling its use for predicting NUP at the field scale. The overarching goal of this work was to use supervised learning regression algorithms—Lasso, support vector regression (SVR), random forest, and partial least squares regression (PLSR)—to predict early season (i.e., V6–V14) maize NUP at three experimental sites in Minnesota using high-resolution hyperspectral imagery. In addition to the spectral features offered by hyperspectral imaging, the 10th percentile Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) was made available to the learning models as an auxiliary feature to assess its ability to improve NUP prediction accuracy. The trained models demonstrated robustness by maintaining satisfactory prediction accuracy across locations, pixel sizes, development stages, and a broad range of NUP values (4.8 to 182 kg ha−1). Using the four most informative spectral features in addition to the auxiliary feature, the mean absolute error (MAE) of Lasso, SVR, and PLSR models (9.4, 9.7, and 9.5 kg ha−1, respectively) was lower than that of random forest (11.2 kg ha−1). The relative MAE for the Lasso, SVR, PLSR, and random forest models was 16.5%, 17.0%, 16.6%, and 19.6%, respectively. The inclusion of the auxiliary feature not only improved overall prediction accuracy by 1.6 kg ha−1 (14%) across all models, but it also reduced the number of input features required to reach optimal performance. The variance of predicted NUP increased as the measured NUP increased (MAE of the Lasso model increased from 4.0 to 12.1 kg ha−1 for measured NUP less than 25 kg ha−1 and greater than 100 kg ha−1, respectively). The most influential spectral features were oftentimes adjacent to each other (i.e., within approximately 6 nm), indicating the importance of both spectral precision and derivative spectra around key wavelengths for explaining NUP. Finally, several challenges and opportunities are discussed regarding the use of these results in the context of improving nitrogen fertilizer management.

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Section: Introductionmentioning

confidence: 99%

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

et al. 2020

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“…MRTN rates are ~10% higher than those reported in the ARMS survey and range from 129 to 209 kg N ha −1 yr −1 (Table 2), for an average rate of 177 (±27 SD) kg N ha −1 yr −1 . Farmers in general 17 and U.S. farmers specifically 32 more often use N fertilizer recommendations from fertilizer and seed dealers than from university extension, and when used, MRTN rates are commonly used as starting points for N rate decisions. We thus set our maximum range to 20% greater than local MRTN values to obtain a plausible bracketing.…”

Section: Resultsmentioning

confidence: 99%

“…The pressure on farmers to increase crop yields for greater economic return often leads to excessive N fertilizer application, despite its economic and environmental cost 1,17–19 . In most of the world, fertilizer is applied uniformly at the beginning of a cropping season in anticipation of high yields and efficient use, even though farmers recognize that yields and N use will be neither uniform nor necessarily efficient in any given year, and that fertilizer not taken up by crops will be lost from fields, thereby lowering profits and harming the environment.…”

Section: Introductionmentioning

confidence: 99%

Yield stability analysis reveals sources of large-scale nitrogen loss from the US Midwest

Basso

Shuai

Zhang

et al. 2019

Sci Rep

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Loss of reactive nitrogen (N) from agricultural fields in the U.S. Midwest is a principal cause of the persistent hypoxic zone in the Gulf of Mexico. We used eight years of high resolution satellite imagery, field boundaries, crop data layers, and yield stability classes to estimate the proportion of N fertilizer removed in harvest (NUE) versus left as surplus N in 8 million corn ( Zea mays ) fields at subfield resolutions of 30 × 30 m (0.09 ha) across 30 million ha of 10 Midwest states. On average, 26% of subfields in the region could be classified as stable low yield, 28% as unstable (low yield some years, high others), and 46% as stable high yield. NUE varied from 48% in stable low yield areas to 88% in stable high yield areas. We estimate regional average N losses of 1.12 (0.64–1.67) Tg N y −1 from stable and unstable low yield areas, corresponding to USD 485 (267–702) million dollars of fertilizer value, 79 (45–113) TJ of energy, and greenhouse gas emissions of 6.8 (3.4–10.1) MMT CO 2 equivalents. Matching N fertilizer rates to crop yield stability classes could reduce regional reactive N losses substantially with no impact on crop yields, thereby enhancing the sustainability of corn-based cropping systems.

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“…However, if it is used excessively in such cases, it can pollute rivers and streams, as well as cause greenhouse gas emissions. As a result, excessive deployment, wastes resources and money while also exacerbating environmental problems 11 . Fertilizers, with a energy equivalency of 51.5 percent, were found to have the highest rate of energy equivalency among all the inputs used in maize production 12 Planting window is one of the non-monetary input.…”

Section: Introductionmentioning

confidence: 99%

Productivity, nutrient use efficiency, energetics and bio-economics of winter maize in south India

Salakinkop

Hulamani

2021

Preprint

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Maize area is rapidly spreading in south India in response to rising demand from the poultry and fish feed industries. The planting of maize during winter season is necessary to increase the total area and production of maize. The present investigation encompassing different sowing windows with different fertility levels revealed that significantly higher winter maize productivity was achieved from first and second week of October planting along with application of 200 % RDF(recommended dose of fertilizer) followed by 150 % RDF. Planting of winter maize during first week of October recorded significantly higher grain yield (8786 kg ha-1) and stover yield (1220 kg ha-1) and was found on par with sowing during second week of October. Among fertility levels, significantly higher grain yield (8320 kg ha -1) and stover yield (1195 kg ha-1) were recorded with application of 200 % RDF and was found on par with application of 150 % RDF. Similarly higher dry matter production, more days for physiological maturity, higher accumulation of growing degree days, photo thermal units and heliothermal units were recorded from crop planted during first and second week of October along with application of either 200 % or 150 % RDF. Further higher nutrient use efficiency was recorded from first and second week October planted crop along with lower fertility level (100 % RDF). Similarly significantly higher output energy, net energy and specific energy were higher from crop planted during first week of planting along with application of 200 % RDF. Also it recorded higher net returns and gross returns Whereas, energy use efficiency and energy productivity were higher with planting during first week of October along with application of 100 % RDF.

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Economic perspectives on nitrogen in farming systems: managing trade-offs between production, risk and the environment

Cited by 46 publications

References 50 publications

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery

Yield stability analysis reveals sources of large-scale nitrogen loss from the US Midwest

Productivity, nutrient use efficiency, energetics and bio-economics of winter maize in south India

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