375 extractive question-and-answer pairs built from January, 2021, published by desagri.gov.in. Every answer is a verbatim span of text the source prints, and each row carries the passage it sits in, its offset in that passage, the source quote, the page and the location in the document, so any row can be checked against the original. 127 of the 375 pairs (33.9%) are explanatory questions and 248 restate a figure. 99.47% of rows pass the corpus quality gate.
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Strong coverage with minor gaps in documentation or freshness.
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First 10 of 375 rows
| question | answer | context | answer_start | question_type | knowledge_quality_score | source_quote | source_page | source_location | confidence | validation_status |
|---|---|---|---|---|---|---|---|---|---|---|
| What is described as the backbone of the country, with plans to enhance it through youth engagement and technology? | the backbone of our country and plans are afoot to strengthen this backbone with youth engagement, employment generation, technology & digitization” | It is a proud moment for all of us where young minds will discuss, collaborate, and create some of the best ideas & solutions which will guide us for years to come. Agriculture is the backbone of our country and plans are afoot to strengthen this backbone with youth engagement, employment generation, technology & digitization” | 180 | definition | 1.000 | Agriculture is the backbone of our country and plans are afoot to strengthen this backbone with youth engagement, employment generation, technology & digitization” | 9 | page=9,block=9 | 0.850 | valid |
| What does Pt represent in the econometric model? | current price (`/quintal) of brinjal in period t | If there is any existence of a cycle, periodicity of cycle is noted. iii. Relationships between Market Arrivals and Prices. The arrivals and prices are subjected to change over a period of time due to innovations, supply of more inputs and increase in population. The estimation of time trend on growth of these variables may be helpful in studying the directions of change and in guiding policy formulations. The econometric model used for the purpose can be stated as: Pt = a + bPt-1+cYt + ut where, Pt = current price (`/quintal) of brinjal in period t, Yt = current arrival (quintals) of brinjal in period t, Pt-1 = lagged price of brinjal, a = Intercept b & c = Regression coefficient ut = error term | 507 | definition | 1.000 | The econometric model used for the purpose can be stated as: Pt = a + bPt-1+cYt + ut where, Pt = current price (`/quintal) of brinjal in period t, Yt = current arrival (quintals) of brinjal in period t, Pt-1 = lagged price of brinjal, a = Intercept b & c = Regression coefficient ut = error term | 15 | page=15,block=10 | 0.700 | valid |
| What does Yt represent in the econometric model? | current arrival (quintals) of brinjal in period t | If there is any existence of a cycle, periodicity of cycle is noted. iii. Relationships between Market Arrivals and Prices. The arrivals and prices are subjected to change over a period of time due to innovations, supply of more inputs and increase in population. The estimation of time trend on growth of these variables may be helpful in studying the directions of change and in guiding policy formulations. The econometric model used for the purpose can be stated as: Pt = a + bPt-1+cYt + ut where, Pt = current price (`/quintal) of brinjal in period t, Yt = current arrival (quintals) of brinjal in period t, Pt-1 = lagged price of brinjal, a = Intercept b & c = Regression coefficient ut = error term | 562 | definition | 1.000 | The econometric model used for the purpose can be stated as: Pt = a + bPt-1+cYt + ut where, Pt = current price (`/quintal) of brinjal in period t, Yt = current arrival (quintals) of brinjal in period t, Pt-1 = lagged price of brinjal, a = Intercept b & c = Regression coefficient ut = error term | 15 | page=15,block=10 | 0.700 | valid |
| What does Wt signify in the ARIMA model? | the first difference of the price series of vegetables | moving average model of order q (MA (q)) is illustrated by equation 2. Xt = et – q1et–1 – q2et–2 – ... – qqet–q ...... (2) Where, q1, q2, ......, qp are the coefficients of the model and e t is the white noise. The ARIMA (p,d,q) model in terms of the backward shift operator B can be shown by equation 3. (1 – f1B – ... – fpBp) Wt = (1 – q1B – ... – qqBq) At ...... (3) where, Wt = (1-B)dZt , is the first difference of the price series of vegetables. Zt and At is the random shock which follows a white noise process with mean zero (O’Donovan, 1983 & Pankartx, 1984). The seasonal autoregressive integrated moving average SARIMA (P,Q,D) model in terms of backward shift operator B can be expressed as equation 4 and equation 5. (1 – fsBs – ... – fsPBsP) Wt = (1 – qsBs – ... – qsQBsQ) At ...... (4) Where, Wt = (1-Bs)dZt, s=52 for weekly data; 12 for monthly data and 4 for quarterly data. | 396 | definition | 1.000 | (3) where, Wt = (1-B)dZt , is the first difference of the price series of vegetables. | 21 | page=21,block=2 | 0.700 | valid |
| What does the final term in parentheses of the Akaike’s information criteria formula indicate? | the number of parameters in the model (including s2, the variance of the residuals) | (7) Where, Autoregressive operator of order p = 1 – f1B – ... – fpBp Seasonal autoregressive operator of order P = 1 – fsBs – ... – fsPBsP Moving average operator of order q = 1 – q1B – ... – qqBq Seasonal moving average operator of order Q = 1 – qsBs – ... – qsQBsQ 2.1. Diagnostic measures for evaluation of forecast performance Akaike’s information criteria (AIC) is useful for determining the order of an ARIMA model. It can be written as equation 8. AIC= –2log (L) + 2 (p+q+k+1) ...... (8) Where L is the likelihood of the data, k=1 if c¹0 and k=0 if c=0. The last term in parentheses is the number of parameters in the model (including s2, the variance of the residuals). The corrected Akaike’s information criteria (AICc) is given by equation 9. 2 (p+q+k+1) (p+q+k+2) AICc = AIC + ——————— ...... (9) T–p=q–k–2 The Bayesian information criterion (BIC) is illustrated by equation 10. | 593 | definition | 1.000 | The last term in parentheses is the number of parameters in the model (including s2, the variance of the residuals). | 21 | page=21,block=2 | ||
| What does the variable 'At' signify in the equations? | the actual value of price (` per quintal) | Articles January, 2021 | Agricultural Situation in India | 19 by equation 11. 1 n At – Ft M = — å | —–— | ...... (11) n t=1 At Where, At is the actual value of price (` per quintal) and Ft is the forecast value (` per quintal). The mean squared error or mean squared deviation is the average of squares of the errors, i.e., the average squared difference between the estimated value and the actual value and is shown by equation 12. 1 N MSE = — å (At – Ft)2 ...... (12) N 1 Where, At and Ft are the actual and predicted prices of vegetables in Varanasi market of Uttar Pradesh and N is total number of observations. 2.2. The root mean square error (RMSE) is depicted by equation 13. 1/2 1 n RMSE = { – [å (At – Ft)2]} ...... (13) n t =1 Theil’s inequality coefficient (TIC) or Theil’s U statistic (Theil, 1966) is scale independent and is expressed by equation 14. ...... | 140 | definition | 1.000 | (11) n t=1 At Where, At is the actual value of price (` per quintal) and Ft is the forecast value (` per quintal). | 22 | page=22,block=0 | 0.700 | valid |
| What does the variable 'Ft' denote in the equations? | the forecast value (` per quintal) | Articles January, 2021 | Agricultural Situation in India | 19 by equation 11. 1 n At – Ft M = — å | —–— | ...... (11) n t=1 At Where, At is the actual value of price (` per quintal) and Ft is the forecast value (` per quintal). The mean squared error or mean squared deviation is the average of squares of the errors, i.e., the average squared difference between the estimated value and the actual value and is shown by equation 12. 1 N MSE = — å (At – Ft)2 ...... (12) N 1 Where, At and Ft are the actual and predicted prices of vegetables in Varanasi market of Uttar Pradesh and N is total number of observations. 2.2. The root mean square error (RMSE) is depicted by equation 13. 1/2 1 n RMSE = { – [å (At – Ft)2]} ...... (13) n t =1 Theil’s inequality coefficient (TIC) or Theil’s U statistic (Theil, 1966) is scale independent and is expressed by equation 14. ...... | 192 | definition | 1.000 | (11) n t=1 At Where, At is the actual value of price (` per quintal) and Ft is the forecast value (` per quintal). | 22 | page=22,block=0 | 0.700 | valid |
| What does the symbol *** signify in significance codes? | 1 percent level of significance | Note: Significance codes: *** is 1 percent level of significance, ** indicates 5 percent level of significance; * indicates 10 percent level of significance; ARIMA= Autoregressive Integrated Moving Average; SAR=Seasonal Autoregressive; AR= Autoregressive; MA= Moving Average; AIC=Akaike’s information criteria (AIC); AICc=Corrected Akaike’s information criteria (AICc); and BIC=Bayesian information criteria (BIC) Source: Authors’ own computation | 33 | definition | 1.000 | Note: Significance codes: *** is 1 percent level of significance, ** indicates 5 percent level of significance; * indicates 10 percent level of significance; ARIMA= Autoregressive Integrated Moving Average; SAR=Seasonal Autoregressive; AR= Autoregressive; MA= Moving Average; AIC=Akaike’s information criteria (AIC); AICc=Corrected Akaike’s information criteria (AICc); and BIC=Bayesian information criteria (BIC) Source: Authors’ own computation | 23 | page=23,block=2 | 0.700 | valid |
| What is e t in the autoregressive model? | e t is white noise | The autoregressive model of order p is denoted by AR (p) and is given by equation 1. p Xt = c + å fi Xt–i + et ...... (1) i=1 Where, f1, f2, ......, fp are the parameters of the model, c is a constant and e t is white noise. The | 205 | definition | 1.000 | (1) i=1 Where, f1, f2, ......, fp are the parameters of the model, c is a constant and e t is white noise. | 20 | page=20,block=12 | 0.700 | valid |
| What is Moving average method? | used to estimate seasonal variation and residual method for cyclical variations | 2005 to 2017. Moving average method is used to estimate seasonal variation and residual method for cyclical variations. The simple linear regression analysis is used to study the response of the prices on arrivals of brinjal. | 39 | definition | 0.980 | Moving average method is used to estimate seasonal variation and residual method for cyclical variations. | 14 | page=14,block=9 | 0.850 | valid |
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| 0.700 |
| valid |