A linear programming approach to fitting an upper quadratic boundary line to natural rubber data

Banneheka, B.M.S.G.; Dhanushika, M.P.; Wijesuriya, Wasana; Herath, Kalahe Hewage Iresha Nadeeka Madushani

doi:10.4038/jnsfsr.v41i1.5327

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…Milne et al, 2006). For more information in this regards, readers can refer to Makowski et al,(2007); Banneheka et al (2013); Shatar and McBratney (2004); Riffel (2012); Kitchen et al (2003); Tasistro (2012); Schnug et al (1996), and Huang et al (2008). In this study data points from the upper edge of the data cloud were selected by eye and then an appropriate function was fitted to these points.…”

Section: Yield Gap Assessment Based On Boundary Line Analysismentioning

confidence: 99%

Using boundary line analysis to assess the on-farm crop yield gap of wheat

Hajjarpoor

Soltani

Zeinali

et al. 2018

Field Crops Research

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Food security is one of the most important challenges facing human kind. A very promising approach to solve the problem is closing the yield gap, i.e. the difference between farmer's and potential yield. A 'complete yield gap assessment method' must provide information regarding potential yield, actual yield and yield gap, the causes of the gap and their importance. The objective of this study was to indicate how boundary line analysis (BLA) could be applied to such an assessment. BLA was only applied to crop management practices/inputs, e.g. sowing date and rate and fertilizer applications. The data were gathered from about 700 wheat farms in Golestan province, one of the major wheat producing regions in Iran, during two growing seasons of 2013-2014 and 2014-2015. Wheat production in Golestan province can be divided into three production situations according to agro-and geo-climatology criteria: these are 'irrigated or high-rainfall', 'high-yield rainfed', and 'low-yield rainfed'. Boundary lines were fitted to the edge of the data cloud of crop yield versus management variables using data from each of the three wheat production situations in the province. Actual farmers' yields were 3900 kg ha-1 for irrigated, 4000 kg ha-1 for high-yield rainfed and 2000 kg ha-1 for low-yield-rainfed situations; BLA indicated that potential yields (the highest yields obtained by farmers in the sample) were 6900, 5800 and 3900 kg ha −1 for each situation, respectively. The corresponding yield gaps were high at 42%, 31% and 50%. Using BLA it was possible to determine the optimal sowing date, seeding rate, frequency and amount of nitrogen fertilizer applied, amount of nitrogen top-dressing, amount of phosphorus and potassium fertilizers and irrigation frequency. The percentage of farmers who cultivated outside of the optimal levels was also identified and was used to determine the importance of each management factor in yield gap. It was concluded that BLA as applied in the study, was a cheap and simple method which, without the need for expensive experimentation, was able to detect yield gaps and their causes in a region. The method can be used effectively in countries/regions where important yield gaps exist.

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Section: Yield Gap Assessment Based On Boundary Line Analysismentioning

confidence: 99%

Using boundary line analysis to assess the on-farm crop yield gap of wheat

Hajjarpoor

Soltani

Zeinali

et al. 2018

Field Crops Research

View full text Add to dashboard Cite

show abstract

“…Milne et al, 2006). For more information in this terms, readers can refer to Schnug et al (1996); Kitchen et al (2003); Shatar and McBratney (2004); Makowski et al (2007); Huang et al (2008); Riffel (2012); Tasistro (2012); Banneheka et al (2013) and Patrignani et al (2014). For simplicity, in the present study is the selection of the data points from the outer edge of the data by eye and then fitting an appropriate function to the points; iii) the final step is to fit a function to the data points obtained from the second step.…”

Section: Yield Gap Assessment Based On Boundary Line Analysismentioning

confidence: 99%

Factors causing yield gap in rape seed production in the eastern of Mazandaran province, Iran

et al. 2020

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The reduction of yield gap and achievement yield potential of oil plants make a significant contribution to yield increases and oil production in developing countries. This research was carried out to investigate the factors causing yield gap associated with rape seed crop management in the Neka region, east of the Mazandaran province, Iran, through a field study during 2015-2016 and 2016- 2017. Boundary line analysis (BLA) was only applied to crop management practices/inputs, e.g. sowing date and rate, fertilizer applications, etc. Boundary lines were fitted to the edge of the data cloud of crop yield versus management variables in data. The average yield in 100 farms was 2051 kg/ha. According to findings of BLA, an average yield, based on the optimum level of the 14 studied variables, was 3032 kg/ha with a 981 kg/ha yield gap per hectare. The average relative yield and relative yield gap for the 14 investigated variables were 68.35% and 31.65%, respectively. Therefore, it can be concluded that the use of the boundary line analysis in yield gap studies can clearly show the yield responses to management factors and calculate the possible potentials. Thus, cultivation practice management of the studied variables in farmers’ fields can lead to increased yield and reduced yield gap.

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“…Milne et al, 2006). For more information in this regards, readers can refer to: Schnug et al (1996); Kitchen et al (2003); Shatar and McBratney (2004); Makowski et al (2007); Huang et al (2008); Riffel (2012); Tasistro (2012); Banneheka et al (2013); and Patrignani et al (2014). For simplicity, in the present study is the selection of the data points from the outer edge of the data by eye and then fitting appropriate function to the points.…”

Section: Boundary Line Analysismentioning

confidence: 99%

Large scale assessment of the production process and rice yield gap analysis by comparative performance analysis and boundary-line analysis methods

et al. 2019

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To reduce the yield gap, specifying yield constraints in a particular area is necessary. A complete yield gap assessment method must provide information regarding potential yield, actual yield, and causes of the gap and their importance. Therefore, documenting the production process to explain crop management factors in each area is very important. The objective of the study was to perform a rice yield gap analysis by using comparative performance analysis (CPA) and boundary-line analysis (BLA). Data were gathered from about 100 paddy fields in Neka, eastern Mazandaran province, one of the major rice producing regions in Iran, in 2015 and 2016. All agricultural practices from nursery preparation to harvest have been recorded for improved rice cultivars. CPA focuses on the ability to estimate potential yield and the reason for a yield gap. Boundary lines were fitted to the edge of the data cloud of crop yield versus management variables in data from paddy fields monitoring. The documenting analysis shows that the range of paddy yield in 100 fields varied from 6100 to 8200 kg ha–1. Potential yields were 9241 kg ha–1 for CPA method, and 7999 kg ha–1 for BLA method. Furthermore, yield gap predicted 2047 kg ha–1 for CPA method and 874 kg ha–1 for BLA method. In BLA, the average relative yield and relative yield gap of the 13 investigated variables were 89.75% and 10.25% respectively. These results show the importance of each management factor in yield gap. It was concluded that CPA and BLA as applied in the study is a cheap and simple method that, without the need for expensive experimentation, is able to detect yield gap and its causes in a district. From these results, it can be said that the calculated yield gap is close to the definition given for the utilised yield gap and shows the difference between the actual yield and attainable yield in relation to the environmental conditions of the region.

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A linear programming approach to fitting an upper quadratic boundary line to natural rubber data

Cited by 6 publications

References 1 publication

Using boundary line analysis to assess the on-farm crop yield gap of wheat

Using boundary line analysis to assess the on-farm crop yield gap of wheat

Factors causing yield gap in rape seed production in the eastern of Mazandaran province, Iran

Large scale assessment of the production process and rice yield gap analysis by comparative performance analysis and boundary-line analysis methods

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