Sustainable agri-food production
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Browsing Sustainable agri-food production by Subject "Agriculture"
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Item Application of metal oxide semiconductor for detection of ammonia emissions from agricultural sources(Elsevier, 2022-11-21) Molleman, Bastiaan; Alessi, Enrico; Krol, Dominika; Morton, Phoebe; Daly, KarenAgricultural emissions of ammonia (NH3) reduce air quality and biodiversity. Measuring the effectiveness of mitigations measures requires rapid monitoring tools, however, conventional methods are labour intensive and costly. This study evaluated the performance of a prototype metal oxide semiconductor (MOS) gas sensor for monitoring NH3. Conventional methods were used to calibrate sensor conductance. The metal oxide semiconductor (MOS) gas sensor was calibrated against NH3 released from a 0.1 M phosphate buffer spiked with ammonium chloride and NH3 released from recently spread cattle slurry. Field measurements using the MOS sensor were compared with values measuring a Bruker Open Path Air Monitoring System. Sensor conductance and NH3 concentration were described using single site Langmuir adsorption model. Field calibrations suggest a higher detection limit above 0.1 ppm and coefficients of determination were 0.93 and 0.89 for sensors 1 and 2, respectively. For prototypes deployed under field conditions, sensitivities of 2.2 and 2.4 with nonlinearity constants of 0.53 and 0.51, were found for sensor 1 and 3 respectively. Average R2 values were 0.88 for sensor 1 and 0.92 for sensor 3. The calibrations were used to calculate NH3 concentrations from slurry emissions using MOS sensor conductance. NH3 concentrations between 0.2 and 1 ppm, were measured with standard deviation of 20% of verified concentrations. The MOS sensor is sensitive enough to detect NH3 emission from agricultural sources with concentrations above 0.2 ppm. Low power and cost of MOS sensors are an advantage over existing techniques.Item Does agricultural cooperative membership impact technical efficiency of maize production in Nigeria: An analysis correcting for biases from observed and unobserved attributes(Public Library of Science, 2021-01-22) Olagunju, Kehinde Oluseyi; Ogunniyi, Adebayo Isaiah; Oyetunde-Usman, Zainab; Omotayo, Abiodun Olusola; Awotide, Bola AmokeThe formation of agricultural cooperatives has been widely promoted as an agricultural development policy initiative to help smallholder farmers cope with multiple production and marketing challenges. Using a nationally representative survey dataset of smallholder maize producers from rural Nigeria, this study assesses the impact of agricultural cooperative membership on technical efficiency (TE). We based our estimation approach on the combination of a newly developed sample selection stochastic production frontier model with propensity score matching to control for possible selectivity biases from both observables and unobservables. We estimate stochastic meta-frontiers to examine TE differences between cooperative members and non-members. Our results reveal that TE levels of members are consistently higher than that of non-members. This calls for continued policy incentives targeted at encouraging farmers to form as well as participate in agricultural cooperatives.