Energy use efficiency and greenhouse gas emissions(GHG)analysis of garlic cultivation in Turkey

This study has been conducted with the purpose of determining energy use efficiency and greenhouse gas emissions of garlic cultivation during the 2020-2021 cultivation season in Adıyaman province of Turkey.Questionnaires,observations and field works were performed in 134 garlic farms in the region through simple random method.In garlic cultivation,energy input was calculated as 32103.20 MJ/hm^(2)and energy output was calculated as 30096 MJ/hm^(2).With regards to the three highest inputs in garlic production,46.66%of the energy inputs consisted of chemical fertilizers energy(14979.26 MJ/hm^(2)),11.29%consisted of farmyard manure energy(3625.71 MJ/hm^(2))and 10.48%consisted of human labour energy(3363.36 MJ/hm^(2)).Energy use efficiency,specific energy,energy productivity and net energy in garlic cultivation were calculated as 0.94,1.71 MJ/kg,0.59 kg/MJ,and−2007.20 MJ/hm^(2),respectively.The total energy input consumed in garlic cultivation was classified as 27.19%direct energy,72.81%indirect energy,35.17%renewable energy and 64.87%nonrenewable energy.Total GHG emissions and GHG ratio were calculated as 8636.60 kg CO_(2)-eq/hm^(2)and 0.46 kg CO_(2)-eq/kg,respectively.

Determination of energy use efficiency and greenhouse gas emissions in peach production

The purpose of this study was to determine the energy use efficiency and greenhouse gas(GHG)emissions in peach production that took place in Kırklareli province of Turkey during the 2020-2021 production season.This study included calculations of energy input,energy output,energy use efficiency,specific energy,energy productivity,net energy,energy input types,GHG emissions and GHG ratio.Survey,observation and data calculations are related to the 2020-2021 production season.The data obtained from the study were collected from 16 different farms(reachable)through face-to-face surveys with full count method.Energy input and energy output were calculated as 19570.58 MJ/hm^(2) and 19471.94 MJ/hm^(2),respectively.With regards to production inputs,55.70% of the energy inputs consisted of chemical fertilizers energy(10900.03 MJ/hm^(2)),9.46% consisted of chemicals energy(1852.10 MJ/hm^(2)),9.32% consisted of human labour energy(1823.13 MJ/hm^(2)),7.65% consisted of electricity energy(1497.28 MJ/hm^(2)),6.91% consisted of diesel fuel energy(1351.52 MJ/hm^(2)),4.73% consisted of irrigation water energy(926.10 MJ/hm^(2)),3.43% consisted of machinery energy(671.98 MJ/hm^(2)),1.88% consisted of transportation energy(367.72 MJ/hm^(2)),0.88% consisted of farmyard manure energy(171.80 MJ/hm^(2))and 0.05%consisted of lime energy(8.94 MJ/hm^(2)).Energy use efficiency,specific energy,energy productivity and net energy were calculated as 0.99,1.91 MJ/kg,0.52 kg/MJ and-98.64 MJ/hm^(2),respectively.The consumed total energy input in production was classified as 28.60% direct energy,71.40% indirect energy,14.93% renewable energy and 85.07% non-renewable.Total GHG emissions and GHG ratio were calculated as 1683.24 kgCO_(2)-eq/hm^(2) and 0.16 kg CO_(2)-eq/kg,respectively.

Classification of the firmness of peaches by sensor fusion

The objectives of this research were to compare the performance of each individual nondestructive sensor with the destructive sensor,and to apply sensor fusion technique to explore whether a combination of sensors would give better results than a single sensor for classification of peach firmness.Tests were carried out with four peach varieties namely Royal Glory,Caterina,Tirrenia and Suidring.In this research,the three nondestructive firmness sensors acoustic firmness,low-mass impact and micro-deformation impact were used to measure firmness.A Bayesian classifier was chosen to provide a classification into three categories,namely soft,intermediate and hard.High level fusion technique was performed by using identity declaration provided by each sensor.The data fusion system processed the information of the sensors to output the fused data.The result of the high level fusion was compared with the classification provided by an unsupervised algorithm based on destructive reference measurement.The fusion process of the nondestructive sensors provided some improvements in the firmness classification;the error rate varied from 25%to 19%for individual sensor.Furthermore,the results of fusion process by using three sensors decreased the error rate from 19%to 13%.This research demonstrated that the fused systems provided more complete and complementary information and,thus,were more effective than individual sensors in the firmness classification of peaches.

Prediction of design parameters of pneumatic cleaners with MARS method

One of the cleaning methods for agricultural materials is based on aerodynamic properties.Pneumatic cleaners are developed on this method.The purpose of this study is to predict the parameters such as fan angle,air velocity,and tunnel length,which are used in the design of pneumatic cleaners,through the multivariate adaptive regression splines(MARS)method.Some parameters have been estimated using the MARS method in order to use pneumatic cleaners under optimum conditions and adapt them to automation systems.The cleaners have a collection box which was installed at the outlet of the storage.Two different product collection boxes of 400 mm(defined as the first box)and 800 mm(defined as the second box)from the storage outlet section were used.From the results obtained,it was observed that the first box R2 was higher.When looking at the cross validation,it was observed that the results of the first box were more acceptable.With this study,MARS equations were used to obtain dependent variables at desired values.Using these equations,independent variables have been demonstrated to be identifiable.In the application results obtained,cleaning efficiency values were obtained in a wide range.While cleaning efficiency values reached up to 100%,the loss rate was found to be very high.Independent variables have been made identifiable to reduce the loss rate.The highest and feasible of these values were determined by MARS as 41°fan angle and 15 m/s air velocity in order to be able to apply at 97%CE and 1%LR determined for the first box.The MARS method allows for the use of more dependent and independent variables.Usable equations were obtained as a result of statistical analysis.More precise values can be obtained with these equations.It will contribute to the design of the parameters of the machine manufactured,such as speed,angle,and feeding amount.