Farm Machinery Operation Research Articles

Evaluation and Optimization of Hybrid Renewable Energy Systems for Sustainable Farm Machinery Operations in Northern Nigeria

This study aimed to assess and optimize hybrid renewable energy systems (HRES) integrating solar and wind energy for sustainable farm machinery operations in Northern Nigeria. A purposive sampling technique was used to select representative farms and agricultural sites within the Kaduna region. Qualitative data were collected through interviews and surveys with farm owners and operators to gain insights into energy use practices, challenges, and perceptions regarding renewable energy adoption. The comparative analysis of different HRES configurations revealed that Configuration C, with the highest solar (20 kW) and wind capacities (15 kW), achieved the highest renewable energy fraction of 90% and the lowest annual cost of $14,800. Configuration B, with increased capacities (15 kW solar and 10 kW wind) and a battery capacity of 200 kWh, achieved an 85% renewable fraction and reduced the annual cost to $16,200. The cost-benefit analysis confirmed that HRES are economically viable and offer significant environmental advantages compared to conventional diesel generators. These findings underscore the potential for optimizing energy resources to achieve higher renewable energy fractions and lower operational costs. The study provides practical recommendations for promoting HRES adoption, highlighting the necessity for financial support, technical training, and robust policy frameworks to facilitate the transition towards sustainable energy solutions in agriculture.

Design and Test of Intelligent Farm Machinery Operation Control Platform for Unmanned Farms

The unmanned farm control platform is of great significance in promoting the supervision of farm production with less manpower or autonomous operation of farm machinery and the construction of farm informatization. Addressing the existing control platform for farm location information acquisition is time-consuming, labor-intensive, and lacks the whole process control of multiple types of farm machinery. In this paper, we propose an Internet of Things (IoT) control scheme for intelligent farm machinery operation of unmanned farms and design the access standards for multiple types of farm machinery, as well as realize the remote control of intelligent farm machinery operation by constructing a remote control model. A high-precision map construction method is designed to improve the DeepLabV3+ algorithm to identify fields and roads. The control models of path planning, remote task, remote control, and safety system are built to achieve the remote control of intelligent agricultural machinery operation. The proposed technology is implemented in the platform integration and application tests are carried out. The error of the constructed high-precision map is less than 3 cm, the completeness rate of the automatic boundary extraction rate is 96.71%, and the correctness rate is 95.63%, which can be used to obtain the boundary instead of manual labeling or on-site point picking. The use of the platform for the simultaneous control of three farm machinery operations reduces the number of people in operation and production and reduces the professional requirements of the personnel, which will promote the management of the entire farm by one person or even by no one in the future.

Accurate recognition of rice plants based on visual and tactile sensing.

Crop recognition is the basis of intelligent agricultural machine operations. Visual perception methods have achieved high recognition accuracy. However, the reliability of such methods is difficult to guarantee because of the complex environment of paddy fields. Tactile sensing methods are not affected by background or environmental interference, and have high reliability. However, in an ideal environment, the recognition accuracy is not as high as that of the visual method. To balance the accuracy and reliability of rice plant recognition, a combined visual-tactile method was proposed in this study. A rice plant recognition device was developed with a poly(vinylidene fluoride) sensor embedded inside the device as a tactile perceptron and a graphic designed as a visual perceptron. The primary role of the tactile perceptron is to initially recognize rice plants and provide a time point for image capture for visual perception. The main role of the visual perceptron is to extract features from the captured images and recognize rice plants again. The results of tactile and visual recognition were eventually fused to achieve accurate recognition of rice plants. The contact speed between the recognition perceptron and rice-weed was selected for the field performance test based on the real situation of paddy field operation. The results showed that the accuracy and reliability of rice plant recognition decreased as the travelling speed of the paddy field operation machine increased. The results of this study provide a basis for intelligent farm machinery operations in rice fields. © 2024 Society of Chemical Industry.

The Impact of Agricultural Machinery Socialization Services on the Scale of Land Operation: Evidence from Rural China

The aims of this study were to analyze in depth the impact of agricultural machinery socialization services on the scale of land operation and to examine whether agricultural machinery socialization services can effectively alleviate the constraints faced by the scale of land operation. Accordingly, a systematic theoretical analysis and derivation of a farmer production model were first conducted to show that the adoption of agricultural machinery socialization services by farmers alleviated the financial, technical, and labor constraints they faced and facilitated the expansion of the farming scale. Subsequently, the OLS model, three-stage least squares, seemingly uncorrelated Biprobit association model, and Probit model were used based on survey data of maize farmers in 13 Chinese provinces in 2019. The endogeneity of simultaneous decision-making between farmers’ adoption of farm machinery socialization services and operation scale was assessed. Then, we empirically tested the effects of agricultural machinery socialization services on the operation scale under different part-time situations and terrain conditions. The empirical study shows that there is a significant contribution of farmers’ adoption of agricultural machinery socialization services to the scale of land operation. The adoption of agricultural machinery socialization services by purely agricultural household groups has a more significant effect on increasing the scale of land management. Moreover, the adoption of agricultural machinery socialization services significantly increases the scale of land management only among farmers on flat land as compared to farmers on sloping land. The research results help to dissect the relationship between agricultural machinery socialization services and operation scale and provide insights for developing agricultural machinery socialization service systems and expanding operation scale.

Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields

The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are needed to realize unmanned rice farming. In the context of unmanned farm machinery operation, the complicated hard bottom layer situation makes it difficult to quantify the local characteristics of paddy fields. In this paper, an unmanned direct rice seeding machine chassis is used to maneuver the operation field and collect the hard bottom layer information simultaneously. This information is used to design a data processing method that automatically calibrates the sensor installation error and performs abnormal value rejection and 3D sample curve denoising of the contour trajectory. A hard bottom layer surface profile evaluation method based on the local sliding surface roughness is also proposed. The local characteristics of the hard bottom layer were quantified, and the results from the test plots showed that the mean value of the local roughness was 0.0065, where 68.27% of the plots were distributed in the variation range of 0.0042~0.0087 and 99.73% were distributed in the variation range of 0~0.0133. Using the test field data, the surface roughness features were verified to describe the variability in representative working conditions, such as the transport, downfield, operation, and trapping of unmanned intelligent farm machinery. When driving intelligent farm machinery, the proposed method for quantifying local features of the hard bottom layer can provide feedback on the local environmental features at any given position of the machinery. The method also provides a reference for the design optimization of unmanned systems, which can help to realize speed adaption and improve the local path tracking control accuracy of smart farming machines.

HBRNet: Boundary Enhancement Segmentation Network for Cropland Extraction in High-Resolution Remote Sensing Images

Cropland extraction has great significance in crop area statistics, intelligent farm machinery operations, agricultural yield estimates, and so on. Semantic segmentation is widely applied to remote sensing image cropland extraction. Traditional semantic segmentation methods using convolutional networks result in a lack of contextual and boundary information when extracting large areas of cropland. In this paper, we propose a boundary enhancement segmentation network for cropland extraction in high-resolution remote sensing images (HBRNet). HBRNet uses Swin Transformer with the pyramidal hierarchy as the backbone to enhance the boundary details while obtaining context. We separate the boundary features and body features from the low-level features, and then perform a boundary detail enhancement module (BDE) on the high-level features. Endeavoring to fuse the boundary features and body features, the module for interaction between boundary information and body information (IBBM) is proposed. We select remote sensing images containing large-scale cropland in Yizheng City, Jiangsu Province as the Agricultural dataset for cropland extraction. Our algorithm is applied to the Agriculture dataset to extract cropland with mIoU of 79.61%, OA of 89.4%, and IoU of 84.59% for cropland. In addition, we conduct experiments on the DeepGlobe, which focuses on the rural areas and has a diversity of cropland cover types. The experimental results indicate that HBRNet improves the segmentation performance of the cropland.

Carbon and Nitrogen Footprints of Major Cereal Crop Production in China: A Study Based on Farm Management Surveys

Greenhouse gas (GHG) emissions and reactive nitrogen (Nr) releases are central environmental problems, which are closely linked to climate change, environmental ecology and crop production. Sustainable development of agriculture plays an important role in GHG emissions and Nr loss. The life cycle assessment (LCA) method was used to calculate the product and farm carbon footprints (CFs) and nitrogen footprints (NFs) in rice, wheat and maize production in China based on farm survey data. The results pinpointed that the CFs of rice, wheat and maize were 0.87, 0.30 and 0.24 kg/kg. Meanwhile, the computed NFs were 17.11, 14.26 and 6.83 g/kg, respectively. Synthetic nitrogen fertilizer applications and methane (CH4) emissions were dominant CF sources, while ammonia (NH3) volatilization was the main NF contributor. Moreover, significant decreases in CF and NF by 20%–54% and 33%–61%, respectively, were found in large-size farms (> 20 hm2) when compared to small-size farms (< 0.7 hm2). Furthermore, the significantly positive relationships between CF and NF indicated the potential for simultaneous mitigation in the regions with high agricultural inputs, like amounts of fertilizer. Based on our results, some effective solutions would be favorable toward mitigating climate change and eutrophication of the major cereal crop production in China, especially optimizing fertilizer use and farm machinery operation efficiencies, as well as developing large-size farms with intensive farming.

Definition of Reference Models for Power, Mass, Working Width, and Price for Tillage Implements

Farm machinery selection, operation and management directly impact crop cultivation processes and outputs. A priori quantification of technical and financial needs allows definition of proportionate distribution and management of available resources and simplification of selection process. Appropriate planning, association and adjustment of the power unit and implement are required for soil cultivation. Consideration of functional parameters of the implement, their proper estimation and operation directly impact the soil structure, productivity and return on investment. Thus, a modelling approach was implemented for the definition of possible parameter-price relations for tillage equipment. The performed analysis allowed us to investigate the main relevant parameters, quantify their impact, and elaborate forecasting models for price, power, mass and working width. The significant relevance of the technical parameters and adjustment issues were outlined for each tillage implement group. For harrows and cultivators, the dependencies between studied parameters expressed better predictive qualities, especially for price-mass relation (R² &gt; 0.8). While for ploughs power and mass relation had a primary output (R² = 0.7). The prediction features of the models provided reliable results for the estimation of the indicative values of the price and parameters of the implements.

Effects of multi-cropping system on temporal and spatial distribution of carbon and nitrogen footprint of major crops in China

Life cycle assessments (LCA) of staple food (rice, wheat and corn) production and assessments of the associated greenhouse gas (GHG) and reactive nitrogen (Nr) releases from environmental and ecological perspectives help to develop effective mitigation options. Therefore, carbon (C) and nitrogen (N) footprint reductions in agroecosystems have become an increasingly popular topic related to global climate change and agricultural adaptation. The LCA method was used to calculate the product and farm C footprint (CF) and N footprint (NF) of double rice, rice-wheat and wheat-maize production based on governmental statistical datasets and published results. The spatial and temporal patterns of CF and NF were analyzed in China during 2004–2017, and their driving factors were analyzed to identify potential mitigation strategies. The results showed that of the various inputs, fertilizer application and diesel oil consumption contributed the most to both GHG and Nr emissions from farm inputs in the grain crop production process. The CFs for double rice, rice-wheat and wheat-maize were 0.83, 0.74, and 0.37 kg CO2-eq kg−1 year−1 at the yield scale, respectively. In addition, the NFs were 11.6, 13.4, and 15.4 gN-eq kg−1 year−1 at the yield scale for double rice, rice-wheat and wheat-maize, respectively. The largest fraction of CF and NF was the share of CH4 emissions and NH3 volatilization from the field soil, respectively. The annual CFs and NFs of the multiple crop in the southwestern provinces were higher than those in the central and northern provinces. The annual GHG and Nr emissions from the multiple crop maintained a relatively stable state from 2004 to 2017. The proportion of CO2/Nr production was partly reflected a soil C/N ratio, suggesting a higher C abundance for the double rice production, which could be used as a reference parameter for crop structure adjustment in the future. Based on our results and other studies, some effective solutions, especially optimized fertilization, farm machinery operation efficiencies and changes in regional allocation of grain cropping areas, are needed to mitigate the impacts of climate change and eutrophication on the main grain crop production in China.

Soil physical properties, nitrogen uptake and grain quality of maize (Zea mays L.) as affected by tillage systems and nitrogen application

Soil compaction is a global issue pertaining to agricultural lands. The frequent use of farm machinery and field operations at the same depth are the major causes of soil compaction. The gradual increase in soil compaction deteriorates maize grain quality due to reduced nitrogen (N) uptake. Quality food production by reducing soil compaction is the need of time, which can be achieved through deep tillage and N management. In this study, three tillage systems viz. conventional tillage (using cultivator), tillage with mould board plough +2-cultivations (MBP), and tillage with chisel plough +2-cultivations (CP); and three nitrogen levels viz. 100, 150 and 200 kg ha–1 were used to evaluate their effect on soil properties, N uptake and grain quality in maize. Lower bulk density (1.41 Mg m–3), higher total porosity (0.47 m3 m–3) and higher nitrogen uptake (96.01 kg ha–1) was recorded under chisel plough (CP) compared with other tillage systems. Different N levels had significant effect on grain and total N uptake and grain quality, while soil properties remains unaffected. Higher N uptake was recorded with 200 kg ha–1 N application than other treatments. Similarly, 8.51% and 8.57% more grain protein contents were recorded with 200 kg ha–1 N during first and second year respectively. Unlike grain protein, starch and oil contents were negatively affected by N application being higher starch (71.7%) and oil contents (3.41%) with less N supply (100 kg ha–1). However, interaction effect of tillage and nitrogen levels was found non-significant for all studied parameters except for oil contents. Higher oil contents were recorded with MBP along with 100 kg ha–1 N application. Overall study indicated that deep ploughing with CP is important for maize to explore more soil area for nutrient uptake and N is also important for improving grain quality especially protein contents an important food constituent.

Classification and evaluation of uncertain influence factors for farm machinery service

Uncertainty extremely interferes with the execution of farm machinery operation. Treating uncertainties is especially important for machinery cooperatives providing social service since they face more uncertain influence factors (UIFs) than family farms. Under social service circumstance, uncertainties may arise from participants and environments. Classification and evaluation of UIFs were studied in this research. According to the production system, 32 UIFs are defined and classified into six categories, which include supply, demand, interactivity, nature, society and others. Uncertainty composite index (UCI) is defined to evaluate the importance of UIFs, which is the square root of the product of occurrence frequency (OF) and impact degree (ID) calculated from the well-designed questionnaire responded by farm machinery operators. UCI is divided into five ranks based on normalization distribution test to illustrate the level of importance. Results from questionnaire showed that natural UIFs have an extreme impact on farm operation, UIFs of the demand and the supply have a serious influence on farm operation, UIFs of interactivity cannot be ignored, and social UIFs have a weak impact on farm operations. This study discovered the uncertainty problems under the specific circumstance of farm machinery service, which may provide a theoretical basis and potential methods for risk management of machinery cooperatives. Keywords: uncertainty, uncertain influence factor (UIF), classification, uncertainty composite index (UCI), machinery cooperatives DOI: 10.25165/j.ijabe.20171006.3045 Citation: Wu C C, Cai Y P, Hu B B, Wang J. Classification and evaluation of uncertain influence factors for farm machinery service. Int J Agric & Biol Eng, 2017;10(6):164–174.

Association Between Farm Machinery Operation and Low Back Disorder in Farmers: A Retrospective Cohort Study.

The association between whole body vibration (WBV) as measured by annual accumulated use of all-terrain vehicles (ATV)/combine/tractor operation and low back disorders (LBDs) among farmers was investigated. Saskatchewan Farm Injury Cohort Study data was used. Baseline data were collected in 2007 on the three vehicle types and other factors. Follow-up data on LBD symptoms were collected during 2013 resulting in 1149 samples. Adjusted for age, education, and gender, LBDs were associated with tractor operation for 1 to 150 hours/year (Relative Risk [RR] = 1.23, 95%CI 1.05 to 1.44), 151 to 400 hours/year (RR = 1.32, 95%CI 114 to 1.54) and 401+ hours/year (RR = 1.34, 95%CI 1.15 to 1.56). Additionally, hip symptoms were associated with tractor operation. Only unadjusted associations were found in combine and ATV operation. Duration of tractor operation and older age are important predictors of both low back and hip symptoms in farmers.

Carbon and nitrogen footprint of double rice production in Southern China

Agriculture plays an important role in greenhouse gases (GHGs) emissions and reactive nitrogen (Nr) loss. Therefore, carbon (C) and nitrogen (N) footprint reductions in agro-ecosystem have become an increasingly hot topic in global climate change and agricultural adaptation. The objective of this study was to assess the C footprint (CF) and N footprint (NF) of double rice (Oryza sativa L.) production using life cycle assessment method in Southern China. The results showed that fertilizer application and farm machinery operation contributed the most to both GHGs and Nr emissions from agricultural inputs in the double rice production process. The CF for the early, late, and double rice was 0.86, 0.83, and 0.85kgCO2-eqkg−1year−1 at yield-scale, respectively. In addition, the NF was 10.47, 10.89, and 10.68gN-eqkg−1year−1 at yield-scale for the early, late and double rice, respectively. The largest fraction of CF and NF of double rice was the share of CH4 emission and NH3 volatilization from the paddy field, respectively. Higher CF and NF at yield-scale for Guangdong, Guangxi, and Hainan provinces were presented, compared to the average level in double rice cropping for the region, while smaller than those of Jiangxi, Hubei, and Hunan provinces. Some effective solutions would be favorable toward mitigating climate change and eutrophication of the double rice cropping region in Southern China, including reduction of fertilizer application rates, improvements in farm machinery operation efficiencies, and changes in regional allocation of double rice cropping areas.

Addressing Uncertainty in Temporal and Spatial Scheduling for Farm Machinery Operation

Uncertainty is an important characteristic of scheduling model for scale farm machinery operation organizing. Practice shows that scheduling model without considering uncertainties is nearly useless. Uncertain influence factors arisen from natural environment, society and economy, market and supply, and customer and behavior, exist widely, emerge frequently, and affect production deeply. Uncertainties interfere with the allocation of productive factors on temporal and spatial dimensions for farm machinery operation scheduling and management. Questionnaire for farm machinery organizations was designed and finished in 2014. Both occurrence frequency and influence degree for each factor were quantified. Four influence factors including operation location change, weather mutation, parts supply delay, and operation skill defects appear in both list of high occurrence and deep influence. Then results of questionnaire and results of specific investigation were used to study temporal and spatial scheduling model and system for farm machinery management. Three case studies are introduced. The first case is about the uncertainty and countermeasure of forage harvesters scheduling and monitoring for a professional forage plantation company. The second case is about the uncertainty and counter measure of cotton-picker scheduling and monitoring for a professional cotton picking company. And the third case is about the uncertainty and countermeasure of social service management for a professional cooperative. The cases show that the research has strong pertinence to deal with uncertainties and can improve management efficiency of farm machinery operation.

Implementation Web Decision Support Model for Predicting Performance of Field Machinery Operation (DWDSS)

Farm machinery planning, design and operation are complicated undertaking due to time and cost constraint and due to prevalence of complicated interacting and overlapping field operations involving capacity constraints and cooperating units. The classical DSS models that applied in the past to machinery planning and policy analysis as well as to performance assessment and simulation of machinery demand, and supplies are criticized by limitations in programming and the difficulty in manipulation and storing the bulky data usually encountered in machinery records. In contrast by application of a web-based decision support system (DWDSS) the user can enjoy the facility to store the data in the server. (DWDSS), is a user-friendly interactive program which permits the user to interact by entering the required input records. The model estimates machinery performance of various farm machines. It consists of one model, which helps the farm manager to take the correct optimum selection of his agricultural machinery. DWDSS predicts field efficiency, field capacity, draft power required to operate machines and PTO power. The DWDSS was successfully validated statistically in comparison to the published data from the ASAE (2009). The comparison indicated that there were no significant differences (probability = 0.05) between them in the calculations that were executed. The DWDSS model was applied to real case conditions in Wad Salma and Rahad irrigated schemes in the central clay plains under similar treatments. The DWDSS results of field efficiency, theoretical field capacity, working rate and draw bar power was found fairly identical to the actual Wad Salma and Rahad data. The results indicated that, generally, the actual field efficiencies of the studied machines were found to be lower by 7% than ASAE published data and t-test comparison between Wad Salma and Rahad schemes in working rate of the three tillage implement, indicated no significant difference between the two means at probability level =0.05. In general, the results indicated that the DWDSS could be applied to any real-life case successfully and with confidence. This is reached by helping the decision maker in planning and operation of a farm fleet by deciding size of farm power.

Deficiency of the Odds Ratio for Common Outcomes

Back to table of contents Previous article Next article Communications and UpdatesFull AccessDeficiency of the Odds Ratio for Common OutcomesJay S. Kaufman, Ph.D., and Sam Harper, Ph.D.Jay S. KaufmanSearch for more papers by this author, Ph.D., and Sam HarperMontreal, QuebecSearch for more papers by this author, Ph.D.Published Online:1 Oct 2012https://doi.org/10.1176/appi.ajp.2012.12060800AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: The article by Chen et al. (1) in the July issue provides a fascinating account of the interaction by genotype in the context of a smoking cessation trial. Participating smokers were randomly assigned either to placebo (N=132) or to various combinations of active treatments (N=941). The authors then reported the proportion with confirmed 7-day abstinence after 8 weeks, stratified by three common haplotypes (defined by two single-nucleotide polymorphisms on chromosome 15 that have established associations with nicotine and cocaine dependence). Despite clear evidence of statistical interaction between haplotype group and successful smoking cessation, the authors’ stated conclusion that “[s]mokers with the high-risk haplotype were three times as likely to respond to pharmacologic cessation treatments as were smokers with the low-risk haplotype” is not correct. As shown in Figure S4 of the online data supplement that accompanies the Chen et al. article, treatment approximately doubled (not tripled) the 8-week abstinence proportion in the high-risk haplotype group, from about 24% to about 50%. In contrast, there was no treatment effect in the low-risk haplotype group.The explanation for this large discrepancy is that the authors made the common error of interpreting odds ratios from logistic regression models as relative risks. This interpretation is not valid for outcomes that are not rare. It has been repeatedly noted in the biomedical literature that this is a serious deficiency of the odds ratio (2, 3), and many authors have therefore urged that for cohort analyses such as those used in the Chen et al. study, the odds ratio is not a parameter of interest and should be avoided (4). It is simple in these settings to estimate risk ratios or differences (5), and this also facilitates useful calculations such as the number needed to treat (i.e., the inverse risk difference) (6). For example, manipulation of these absolute risks shows that the number needed to treat in order to prevent one relapse among the high-risk haplotype subpopulation is approximately four. This is an impressive finding that is completely obscured in the published analysis.Montreal, QuebecThe authors report no financial relationships with commercial interests.References1 Chen LS, Baker TB, Piper ME, Breslau N, Cannon DS, Doheny KF, Gogarten SM, Johnson EO, Saccone NL, Wang JC, Weiss RB, Goate AM, Bierut LJ: Interplay of genetic risk factors (CHRNA5-CHRNA3-CHRNB4) and cessation treatments in smoking cessation success. Am J Psychiatry 2012; 169:735–742Link, Google Scholar2 Altman DG, Deeks JJ, Sackett DL: Odds ratios should be avoided when events are common. BMJ 1998; 317:1318Crossref, Medline, Google Scholar3 Schwartz LM, Woloshin S, Welch HG: Misunderstandings about the effects of race and sex on physicians’ referrals for cardiac catheterization. N Engl J Med 1999; 341:279–283; Discussion 286–287Crossref, Medline, Google Scholar4 Knol MJ, Duijnhoven RG, Grobbee DE, Moons KG, Groenwold RH: Potential misinterpretation of treatment effects due to use of odds ratios and logistic regression in randomized controlled trials. PLoS ONE 2011; 6:e21248Crossref, Medline, Google Scholar5 Austin PC: Absolute risk reductions, relative risks, relative risk reductions, and numbers needed to treat can be obtained from a logistic regression model. J Clin Epidemiol 2010; 63:2–6Crossref, Medline, Google Scholar6 Bender R, Kuss O, Hildebrandt M, Gehrmann U: Estimating adjusted NNT measures in logistic regression analysis. Stat Med 2007; 26:5586–5595Crossref, Medline, Google Scholar FiguresReferencesCited byDetailsCited ByNicotine & Tobacco Research, Vol. 20, No. 4Association Between Farm Machinery Operation and Low Back Disorder in FarmersJournal of Occupational & Environmental Medicine, Vol. 58, No. 6 Volume 169Issue 10 October 2012Pages 1118-1118 Metrics PDF download History Accepted 1 August 2012 Published online 1 October 2012 Published in print 1 October 2012

Warming potential of N2O emissions from rapeseed crop in Northern Spain

Nitrous oxide is a greenhouse gas contributing to global warming. Biocrops have been proposed as an alternative for energy production. Nevertheless, the use of biofuels could be a counterproductive approach to mitigate global warming, as the fuel energy gained from biofuel crops might be offset against the nitrogen inputs and associated N2O emissions. The objective of this work was to determine the warming potential of a rapeseed crop in northern Spain caused by direct N2O emissions and CO2 emitted during farm machinery operations. Mineral fertilizer was applied at a rate of 180kgNha−1. Fluxes of N2O were measured year round. The emission factor of direct N2O losses was determined, together with the relative warming with regard to plant N content and the CO2 emissions from farm equipment operation. The emission factor from direct N2O losses from rapeseed crop was 2.54%. Emissions of CO2 produced by farm machinery in rapeseed crop represented 8.5% of the CO2 equivalents in terms of N2O emitted by the crop. Thus, rapeseed grown in our conditions leads to N2O emissions whose warming effect is compensated by the “saved fossil CO2” of biofuel production.

Global warming contributions from wheat, sheep meat and wool production in Victoria, Australia – a life cycle assessment

This paper compares the life cycle global warming potential of three of Australia’s important agricultural production activities – the production of wheat, meat and wool in grazed subterranean clover (sub-clover) dominant pasture and mixed pasture (perennial ryegrass/phalaris/sub-clover/grass and cape weed) systems. Two major stages are presented in this life cycle assessment (LCA) analysis: pre-farm, and on-farm. The pre-farm stage includes greenhouse gas (GHG) emissions from agricultural machinery, fertilizer, and pesticide production and the emissions from the transportation of these inputs to paddock. The on-farm stage includes GHG emissions due to diesel use in on-farm transport and processing (e.g. seeding, spraying, harvesting, topdressing, sheep shearing), and non-CO 2 (nitrous oxide (N 2O), and methane (CH 4)) emissions from pastures and crop grazing of lambs. The functional unit of this life cycle analysis is the GHG emissions (carbon dioxide equivalents – CO 2 -e) from 1 kg of wheat, sheep meat and wool produced from sub-clover, wheat and mixed pasture plots. The GHG emissions (e.g. CO 2, N 2O and CH 4 emission) from the production, transportation and use of inputs (e.g. fertilizer, pesticide, farm machinery operation) during pre-farm and on-farm stages are also included. The life cycle GHG emissions of 1 kg of wool is significantly higher than that of wheat and sheep meat. The LCA analysis identified that the on-farm stage contributed the most significant portion of total GHG emissions from the production of wheat, sheep meat and wool. This LCA analysis also identified that CH 4 emissions from enteric methane production and from the decomposition of manure accounted for a significant portion of the total emissions from sub-clover and mixed pasture production, whilst N 2O emissions from the soil have been found to be the major source of GHG emissions from wheat production.

Carbon inventory for a cereal cropping system under contrasting tillage, nitrogen fertilisation and stubble management practices

Conservation farming practices are often considered effective measures to increase soil organic C (SOC) sequestration and/or to reduce CO 2 emissions resulting from farm machinery operation. The long-term CO 2 mitigation potentials of no-till (NT) versus conventional till (CT), stubble retention (SR) versus stubble burning (SB) and N fertilisation (NF) versus no N application (N0) as well as their interactions were examined on a Vertosol (Vertisol) in semi-arid subtropical Queensland, Australia by taking into account their impacts on SOC content, crop residue C storage, on-farm fossil fuel consumption and CO 2 emissions associated with N fertiliser application. The experimental site had been cropped with wheat ( Triticum aestivum L.) or barley ( Hordeum vulgare L.) with a summer fallow for 33 years. Where NT, SR or NF was applied alone, no significant effect on SOC was found in the 0–10, 10–20 and 0–20 cm depths. Nonetheless, the treatment effects in the 0–10 cm depth were interactive and maximum SOC sequestration was achieved under the NT + SR + NF treatment. Carbon storage in crop residues decreased substantially during the fallow period, to a range between 0.4 Mg CO 2-e ha −1 under the CT + SB + NF treatment and 2.4 Mg CO 2-e ha −1 under the NT + SR + N0 treatment (CO 2-e stands for CO 2 equivalent). The cumulative fossil fuel CO 2 emission over 33 years was estimated to be 2.2 Mg CO 2-e ha −1 less under NT than under CT systems. Cumulative CO 2 emissions from N fertiliser application amounted to 3.0 Mg CO 2 ha −1. The farm-level C accounting indicated that a net C sequestration of 4.5 Mg CO 2-e was achieved under the NT + SR + NF treatment, whilst net CO 2 emissions ranging from 0.5 to 6.0 Mg CO 2-e ha −1 over 33 years occurred under other treatments.

Development Computer Software for Optimum Scheduling of Farm Machinery Operations in a Sugarcane Plantation

An algorithm for scheduling of the operation of tractors and implements in a sugar cane plantations was developed. The core of the algorithm was an optimization using random search. The schedule was expressed by the combinations of day-shift-tractor-implement-operator-job-field-covering area. To run the algorithm and facilitate data inputting and outputs printing, a prototipe of software named Walsien 1.0 was developed with Visual Basic 4.0TM