| IVOD_ID |
168923 |
| IVOD_URL |
https://ivod.ly.gov.tw/Play/Clip/1M/168923 |
| 日期 |
2026-04-29 |
| 會議資料.會議代碼 |
委員會-11-5-19-11 |
| 會議資料.會議代碼:str |
第11屆第5會期經濟委員會第11次全體委員會議 |
| 會議資料.屆 |
11 |
| 會議資料.會期 |
5 |
| 會議資料.會次 |
11 |
| 會議資料.種類 |
委員會 |
| 會議資料.委員會代碼[0] |
19 |
| 會議資料.委員會代碼:str[0] |
經濟委員會 |
| 會議資料.標題 |
第11屆第5會期經濟委員會第11次全體委員會議 |
| 影片種類 |
Clip |
| 開始時間 |
2026-04-29T10:57:36+08:00 |
| 結束時間 |
2026-04-29T11:08:07+08:00 |
| 影片長度 |
00:10:31 |
| 支援功能[0] |
ai-transcript |
| video_url |
https://ivod-lyvod.cdn.hinet.net/vod_1/_definst_/mp4:1MClips/6301273cb2dfee845301aa5af76d23d24294381b535652e618096c5e05de57c6c358bed1a557d1e65ea18f28b6918d91.mp4/playlist.m3u8 |
| 委員名稱 |
洪毓祥 |
| 委員發言時間 |
10:57:36 - 11:08:07 |
| 會議時間 |
2026-04-29T09:00:00+08:00 |
| 會議名稱 |
立法院第11屆第5會期經濟委員會第11次全體委員會議（事由：一、 審查115年度中央政府總預算案關於農業部及所屬單位預算部分。
二、 審查115年度中央政府總預算案有關第27款直轄市及縣市政府第7項一般性補助款—農業部主管第1目農業部、第2目林業及自然保育署及所屬、第3目農村發展及水土保持署及所屬、第4目漁業署及所屬、第5目動植物防疫檢疫署及所屬、第6目農糧署及所屬、第7目農田水利署部分預算。
三、 審查115年度中央政府總預算案附屬單位預算非營業部分關於農業部主管：農業作業基金、農田水利事業作業基金、農業特別收入基金及農民退休基金。(詢答)
【4月29日及4月30日二天一次會】） |
| transcript.pyannote[0].speaker |
SPEAKER_01 |
| transcript.pyannote[0].start |
0.03096875 |
| transcript.pyannote[0].end |
1.63409375 |
| transcript.pyannote[1].speaker |
SPEAKER_00 |
| transcript.pyannote[1].start |
10.08846875 |
| transcript.pyannote[1].end |
12.65346875 |
| transcript.pyannote[2].speaker |
SPEAKER_01 |
| transcript.pyannote[2].start |
12.33284375 |
| transcript.pyannote[2].end |
13.37909375 |
| transcript.pyannote[3].speaker |
SPEAKER_00 |
| transcript.pyannote[3].start |
19.13346875 |
| transcript.pyannote[3].end |
20.70284375 |
| transcript.pyannote[4].speaker |
SPEAKER_00 |
| transcript.pyannote[4].start |
21.93471875 |
| transcript.pyannote[4].end |
22.82909375 |
| transcript.pyannote[5].speaker |
SPEAKER_00 |
| transcript.pyannote[5].start |
23.21721875 |
| transcript.pyannote[5].end |
26.79471875 |
| transcript.pyannote[6].speaker |
SPEAKER_00 |
| transcript.pyannote[6].start |
27.16596875 |
| transcript.pyannote[6].end |
30.22034375 |
| transcript.pyannote[7].speaker |
SPEAKER_00 |
| transcript.pyannote[7].start |
30.86159375 |
| transcript.pyannote[7].end |
36.31221875 |
| transcript.pyannote[8].speaker |
SPEAKER_00 |
| transcript.pyannote[8].start |
36.78471875 |
| transcript.pyannote[8].end |
39.24846875 |
| transcript.pyannote[9].speaker |
SPEAKER_00 |
| transcript.pyannote[9].start |
40.00784375 |
| transcript.pyannote[9].end |
46.40346875 |
| transcript.pyannote[10].speaker |
SPEAKER_00 |
| transcript.pyannote[10].start |
46.69034375 |
| transcript.pyannote[10].end |
51.51659375 |
| transcript.pyannote[11].speaker |
SPEAKER_00 |
| transcript.pyannote[11].start |
52.03971875 |
| transcript.pyannote[11].end |
64.62846875 |
| transcript.pyannote[12].speaker |
SPEAKER_00 |
| transcript.pyannote[12].start |
65.43846875 |
| transcript.pyannote[12].end |
68.27346875 |
| transcript.pyannote[13].speaker |
SPEAKER_00 |
| transcript.pyannote[13].start |
68.99909375 |
| transcript.pyannote[13].end |
72.49221875 |
| transcript.pyannote[14].speaker |
SPEAKER_00 |
| transcript.pyannote[14].start |
72.64409375 |
| transcript.pyannote[14].end |
78.53346875 |
| transcript.pyannote[15].speaker |
SPEAKER_00 |
| transcript.pyannote[15].start |
78.68534375 |
| transcript.pyannote[15].end |
80.74409375 |
| transcript.pyannote[16].speaker |
SPEAKER_00 |
| transcript.pyannote[16].start |
81.46971875 |
| transcript.pyannote[16].end |
86.66721875 |
| transcript.pyannote[17].speaker |
SPEAKER_00 |
| transcript.pyannote[17].start |
86.98784375 |
| transcript.pyannote[17].end |
97.36596875 |
| transcript.pyannote[18].speaker |
SPEAKER_00 |
| transcript.pyannote[18].start |
97.58534375 |
| transcript.pyannote[18].end |
97.92284375 |
| transcript.pyannote[19].speaker |
SPEAKER_00 |
| transcript.pyannote[19].start |
98.42909375 |
| transcript.pyannote[19].end |
110.59596875 |
| transcript.pyannote[20].speaker |
SPEAKER_01 |
| transcript.pyannote[20].start |
111.00096875 |
| transcript.pyannote[20].end |
113.70096875 |
| transcript.pyannote[21].speaker |
SPEAKER_00 |
| transcript.pyannote[21].start |
111.13596875 |
| transcript.pyannote[21].end |
111.52409375 |
| transcript.pyannote[22].speaker |
SPEAKER_01 |
| transcript.pyannote[22].start |
113.95409375 |
| transcript.pyannote[22].end |
133.54596875 |
| transcript.pyannote[23].speaker |
SPEAKER_00 |
| transcript.pyannote[23].start |
133.96784375 |
| transcript.pyannote[23].end |
143.40096875 |
| transcript.pyannote[24].speaker |
SPEAKER_01 |
| transcript.pyannote[24].start |
142.59096875 |
| transcript.pyannote[24].end |
153.84659375 |
| transcript.pyannote[25].speaker |
SPEAKER_00 |
| transcript.pyannote[25].start |
154.43721875 |
| transcript.pyannote[25].end |
154.87596875 |
| transcript.pyannote[26].speaker |
SPEAKER_00 |
| transcript.pyannote[26].start |
155.33159375 |
| transcript.pyannote[26].end |
155.95596875 |
| transcript.pyannote[27].speaker |
SPEAKER_00 |
| transcript.pyannote[27].start |
156.41159375 |
| transcript.pyannote[27].end |
167.46471875 |
| transcript.pyannote[28].speaker |
SPEAKER_01 |
| transcript.pyannote[28].start |
160.56284375 |
| transcript.pyannote[28].end |
160.64721875 |
| transcript.pyannote[29].speaker |
SPEAKER_01 |
| transcript.pyannote[29].start |
160.71471875 |
| transcript.pyannote[29].end |
160.93409375 |
| transcript.pyannote[30].speaker |
SPEAKER_00 |
| transcript.pyannote[30].start |
168.22409375 |
| transcript.pyannote[30].end |
172.03784375 |
| transcript.pyannote[31].speaker |
SPEAKER_00 |
| transcript.pyannote[31].start |
172.35846875 |
| transcript.pyannote[31].end |
175.68284375 |
| transcript.pyannote[32].speaker |
SPEAKER_00 |
| transcript.pyannote[32].start |
176.47596875 |
| transcript.pyannote[32].end |
178.68659375 |
| transcript.pyannote[33].speaker |
SPEAKER_00 |
| transcript.pyannote[33].start |
179.04096875 |
| transcript.pyannote[33].end |
180.89721875 |
| transcript.pyannote[34].speaker |
SPEAKER_01 |
| transcript.pyannote[34].start |
180.94784375 |
| transcript.pyannote[34].end |
202.98659375 |
| transcript.pyannote[35].speaker |
SPEAKER_00 |
| transcript.pyannote[35].start |
201.92346875 |
| transcript.pyannote[35].end |
202.88534375 |
| transcript.pyannote[36].speaker |
SPEAKER_00 |
| transcript.pyannote[36].start |
202.98659375 |
| transcript.pyannote[36].end |
206.42909375 |
| transcript.pyannote[37].speaker |
SPEAKER_01 |
| transcript.pyannote[37].start |
204.33659375 |
| transcript.pyannote[37].end |
206.74971875 |
| transcript.pyannote[38].speaker |
SPEAKER_00 |
| transcript.pyannote[38].start |
206.56409375 |
| transcript.pyannote[38].end |
211.10346875 |
| transcript.pyannote[39].speaker |
SPEAKER_00 |
| transcript.pyannote[39].start |
211.12034375 |
| transcript.pyannote[39].end |
212.30159375 |
| transcript.pyannote[40].speaker |
SPEAKER_00 |
| transcript.pyannote[40].start |
212.89221875 |
| transcript.pyannote[40].end |
217.27971875 |
| transcript.pyannote[41].speaker |
SPEAKER_00 |
| transcript.pyannote[41].start |
217.66784375 |
| transcript.pyannote[41].end |
223.33784375 |
| transcript.pyannote[42].speaker |
SPEAKER_00 |
| transcript.pyannote[42].start |
223.75971875 |
| transcript.pyannote[42].end |
231.28596875 |
| transcript.pyannote[43].speaker |
SPEAKER_00 |
| transcript.pyannote[43].start |
232.04534375 |
| transcript.pyannote[43].end |
236.14596875 |
| transcript.pyannote[44].speaker |
SPEAKER_00 |
| transcript.pyannote[44].start |
236.71971875 |
| transcript.pyannote[44].end |
242.05221875 |
| transcript.pyannote[45].speaker |
SPEAKER_00 |
| transcript.pyannote[45].start |
242.20409375 |
| transcript.pyannote[45].end |
248.07659375 |
| transcript.pyannote[46].speaker |
SPEAKER_00 |
| transcript.pyannote[46].start |
248.93721875 |
| transcript.pyannote[46].end |
258.38721875 |
| transcript.pyannote[47].speaker |
SPEAKER_00 |
| transcript.pyannote[47].start |
258.60659375 |
| transcript.pyannote[47].end |
268.30971875 |
| transcript.pyannote[48].speaker |
SPEAKER_00 |
| transcript.pyannote[48].start |
268.98471875 |
| transcript.pyannote[48].end |
275.05971875 |
| transcript.pyannote[49].speaker |
SPEAKER_00 |
| transcript.pyannote[49].start |
275.78534375 |
| transcript.pyannote[49].end |
282.06284375 |
| transcript.pyannote[50].speaker |
SPEAKER_01 |
| transcript.pyannote[50].start |
283.09221875 |
| transcript.pyannote[50].end |
316.89284375 |
| transcript.pyannote[51].speaker |
SPEAKER_01 |
| transcript.pyannote[51].start |
317.07846875 |
| transcript.pyannote[51].end |
317.55096875 |
| transcript.pyannote[52].speaker |
SPEAKER_01 |
| transcript.pyannote[52].start |
317.66909375 |
| transcript.pyannote[52].end |
322.95096875 |
| transcript.pyannote[53].speaker |
SPEAKER_00 |
| transcript.pyannote[53].start |
322.54596875 |
| transcript.pyannote[53].end |
332.82284375 |
| transcript.pyannote[54].speaker |
SPEAKER_00 |
| transcript.pyannote[54].start |
333.14346875 |
| transcript.pyannote[54].end |
339.26909375 |
| transcript.pyannote[55].speaker |
SPEAKER_00 |
| transcript.pyannote[55].start |
339.96096875 |
| transcript.pyannote[55].end |
342.66096875 |
| transcript.pyannote[56].speaker |
SPEAKER_00 |
| transcript.pyannote[56].start |
342.96471875 |
| transcript.pyannote[56].end |
349.71471875 |
| transcript.pyannote[57].speaker |
SPEAKER_01 |
| transcript.pyannote[57].start |
348.26346875 |
| transcript.pyannote[57].end |
349.69784375 |
| transcript.pyannote[58].speaker |
SPEAKER_01 |
| transcript.pyannote[58].start |
349.71471875 |
| transcript.pyannote[58].end |
349.73159375 |
| transcript.pyannote[59].speaker |
SPEAKER_00 |
| transcript.pyannote[59].start |
349.73159375 |
| transcript.pyannote[59].end |
357.19034375 |
| transcript.pyannote[60].speaker |
SPEAKER_01 |
| transcript.pyannote[60].start |
350.20409375 |
| transcript.pyannote[60].end |
352.51596875 |
| transcript.pyannote[61].speaker |
SPEAKER_00 |
| transcript.pyannote[61].start |
357.89909375 |
| transcript.pyannote[61].end |
367.16346875 |
| transcript.pyannote[62].speaker |
SPEAKER_00 |
| transcript.pyannote[62].start |
367.33221875 |
| transcript.pyannote[62].end |
371.85471875 |
| transcript.pyannote[63].speaker |
SPEAKER_00 |
| transcript.pyannote[63].start |
372.68159375 |
| transcript.pyannote[63].end |
375.22971875 |
| transcript.pyannote[64].speaker |
SPEAKER_00 |
| transcript.pyannote[64].start |
376.03971875 |
| transcript.pyannote[64].end |
383.26221875 |
| transcript.pyannote[65].speaker |
SPEAKER_00 |
| transcript.pyannote[65].start |
383.48159375 |
| transcript.pyannote[65].end |
384.83159375 |
| transcript.pyannote[66].speaker |
SPEAKER_00 |
| transcript.pyannote[66].start |
385.06784375 |
| transcript.pyannote[66].end |
389.16846875 |
| transcript.pyannote[67].speaker |
SPEAKER_00 |
| transcript.pyannote[67].start |
389.43846875 |
| transcript.pyannote[67].end |
392.69534375 |
| transcript.pyannote[68].speaker |
SPEAKER_00 |
| transcript.pyannote[68].start |
393.37034375 |
| transcript.pyannote[68].end |
399.85034375 |
| transcript.pyannote[69].speaker |
SPEAKER_00 |
| transcript.pyannote[69].start |
400.10346875 |
| transcript.pyannote[69].end |
402.14534375 |
| transcript.pyannote[70].speaker |
SPEAKER_00 |
| transcript.pyannote[70].start |
402.44909375 |
| transcript.pyannote[70].end |
403.22534375 |
| transcript.pyannote[71].speaker |
SPEAKER_00 |
| transcript.pyannote[71].start |
403.81596875 |
| transcript.pyannote[71].end |
404.98034375 |
| transcript.pyannote[72].speaker |
SPEAKER_00 |
| transcript.pyannote[72].start |
405.52034375 |
| transcript.pyannote[72].end |
407.17409375 |
| transcript.pyannote[73].speaker |
SPEAKER_01 |
| transcript.pyannote[73].start |
407.71409375 |
| transcript.pyannote[73].end |
423.94784375 |
| transcript.pyannote[74].speaker |
SPEAKER_00 |
| transcript.pyannote[74].start |
422.31096875 |
| transcript.pyannote[74].end |
424.16721875 |
| transcript.pyannote[75].speaker |
SPEAKER_01 |
| transcript.pyannote[75].start |
424.16721875 |
| transcript.pyannote[75].end |
424.18409375 |
| transcript.pyannote[76].speaker |
SPEAKER_01 |
| transcript.pyannote[76].start |
424.85909375 |
| transcript.pyannote[76].end |
427.57596875 |
| transcript.pyannote[77].speaker |
SPEAKER_00 |
| transcript.pyannote[77].start |
427.57596875 |
| transcript.pyannote[77].end |
430.09034375 |
| transcript.pyannote[78].speaker |
SPEAKER_00 |
| transcript.pyannote[78].start |
430.44471875 |
| transcript.pyannote[78].end |
430.90034375 |
| transcript.pyannote[79].speaker |
SPEAKER_00 |
| transcript.pyannote[79].start |
431.33909375 |
| transcript.pyannote[79].end |
434.08971875 |
| transcript.pyannote[80].speaker |
SPEAKER_00 |
| transcript.pyannote[80].start |
435.25409375 |
| transcript.pyannote[80].end |
436.51971875 |
| transcript.pyannote[81].speaker |
SPEAKER_01 |
| transcript.pyannote[81].start |
437.04284375 |
| transcript.pyannote[81].end |
441.98721875 |
| transcript.pyannote[82].speaker |
SPEAKER_00 |
| transcript.pyannote[82].start |
440.68784375 |
| transcript.pyannote[82].end |
443.06721875 |
| transcript.pyannote[83].speaker |
SPEAKER_01 |
| transcript.pyannote[83].start |
443.82659375 |
| transcript.pyannote[83].end |
448.06221875 |
| transcript.pyannote[84].speaker |
SPEAKER_01 |
| transcript.pyannote[84].start |
448.43346875 |
| transcript.pyannote[84].end |
451.36971875 |
| transcript.pyannote[85].speaker |
SPEAKER_01 |
| transcript.pyannote[85].start |
451.62284375 |
| transcript.pyannote[85].end |
452.56784375 |
| transcript.pyannote[86].speaker |
SPEAKER_00 |
| transcript.pyannote[86].start |
452.75346875 |
| transcript.pyannote[86].end |
453.39471875 |
| transcript.pyannote[87].speaker |
SPEAKER_00 |
| transcript.pyannote[87].start |
453.61409375 |
| transcript.pyannote[87].end |
455.45346875 |
| transcript.pyannote[88].speaker |
SPEAKER_00 |
| transcript.pyannote[88].start |
455.79096875 |
| transcript.pyannote[88].end |
464.81909375 |
| transcript.pyannote[89].speaker |
SPEAKER_00 |
| transcript.pyannote[89].start |
465.12284375 |
| transcript.pyannote[89].end |
469.96596875 |
| transcript.pyannote[90].speaker |
SPEAKER_00 |
| transcript.pyannote[90].start |
470.79284375 |
| transcript.pyannote[90].end |
472.41284375 |
| transcript.pyannote[91].speaker |
SPEAKER_00 |
| transcript.pyannote[91].start |
472.63221875 |
| transcript.pyannote[91].end |
498.72096875 |
| transcript.pyannote[92].speaker |
SPEAKER_00 |
| transcript.pyannote[92].start |
499.12596875 |
| transcript.pyannote[92].end |
502.29846875 |
| transcript.pyannote[93].speaker |
SPEAKER_00 |
| transcript.pyannote[93].start |
503.04096875 |
| transcript.pyannote[93].end |
506.95596875 |
| transcript.pyannote[94].speaker |
SPEAKER_00 |
| transcript.pyannote[94].start |
507.14159375 |
| transcript.pyannote[94].end |
509.09909375 |
| transcript.pyannote[95].speaker |
SPEAKER_00 |
| transcript.pyannote[95].start |
509.23409375 |
| transcript.pyannote[95].end |
518.07659375 |
| transcript.pyannote[96].speaker |
SPEAKER_00 |
| transcript.pyannote[96].start |
518.86971875 |
| transcript.pyannote[96].end |
522.46409375 |
| transcript.pyannote[97].speaker |
SPEAKER_00 |
| transcript.pyannote[97].start |
523.03784375 |
| transcript.pyannote[97].end |
525.80534375 |
| transcript.pyannote[98].speaker |
SPEAKER_00 |
| transcript.pyannote[98].start |
526.17659375 |
| transcript.pyannote[98].end |
534.04034375 |
| transcript.pyannote[99].speaker |
SPEAKER_00 |
| transcript.pyannote[99].start |
534.20909375 |
| transcript.pyannote[99].end |
541.17846875 |
| transcript.pyannote[100].speaker |
SPEAKER_00 |
| transcript.pyannote[100].start |
541.73534375 |
| transcript.pyannote[100].end |
545.78534375 |
| transcript.pyannote[101].speaker |
SPEAKER_00 |
| transcript.pyannote[101].start |
545.97096875 |
| transcript.pyannote[101].end |
547.62471875 |
| transcript.pyannote[102].speaker |
SPEAKER_00 |
| transcript.pyannote[102].start |
548.43471875 |
| transcript.pyannote[102].end |
549.44721875 |
| transcript.pyannote[103].speaker |
SPEAKER_00 |
| transcript.pyannote[103].start |
549.68346875 |
| transcript.pyannote[103].end |
555.31971875 |
| transcript.pyannote[104].speaker |
SPEAKER_01 |
| transcript.pyannote[104].start |
554.20596875 |
| transcript.pyannote[104].end |
562.10346875 |
| transcript.pyannote[105].speaker |
SPEAKER_01 |
| transcript.pyannote[105].start |
562.32284375 |
| transcript.pyannote[105].end |
562.71096875 |
| transcript.pyannote[106].speaker |
SPEAKER_01 |
| transcript.pyannote[106].start |
562.96409375 |
| transcript.pyannote[106].end |
574.16909375 |
| transcript.pyannote[107].speaker |
SPEAKER_00 |
| transcript.pyannote[107].start |
565.79909375 |
| transcript.pyannote[107].end |
565.90034375 |
| transcript.pyannote[108].speaker |
SPEAKER_00 |
| transcript.pyannote[108].start |
573.24096875 |
| transcript.pyannote[108].end |
580.61534375 |
| transcript.pyannote[109].speaker |
SPEAKER_01 |
| transcript.pyannote[109].start |
575.40096875 |
| transcript.pyannote[109].end |
575.87346875 |
| transcript.pyannote[110].speaker |
SPEAKER_00 |
| transcript.pyannote[110].start |
581.08784375 |
| transcript.pyannote[110].end |
589.37346875 |
| transcript.pyannote[111].speaker |
SPEAKER_01 |
| transcript.pyannote[111].start |
583.07909375 |
| transcript.pyannote[111].end |
585.93096875 |
| transcript.pyannote[112].speaker |
SPEAKER_01 |
| transcript.pyannote[112].start |
586.04909375 |
| transcript.pyannote[112].end |
586.40346875 |
| transcript.pyannote[113].speaker |
SPEAKER_00 |
| transcript.pyannote[113].start |
590.68971875 |
| transcript.pyannote[113].end |
592.81596875 |
| transcript.pyannote[114].speaker |
SPEAKER_00 |
| transcript.pyannote[114].start |
593.05221875 |
| transcript.pyannote[114].end |
599.98784375 |
| transcript.pyannote[115].speaker |
SPEAKER_01 |
| transcript.pyannote[115].start |
599.98784375 |
| transcript.pyannote[115].end |
600.27471875 |
| transcript.pyannote[116].speaker |
SPEAKER_00 |
| transcript.pyannote[116].start |
600.27471875 |
| transcript.pyannote[116].end |
604.02096875 |
| transcript.pyannote[117].speaker |
SPEAKER_01 |
| transcript.pyannote[117].start |
604.02096875 |
| transcript.pyannote[117].end |
604.03784375 |
| transcript.pyannote[118].speaker |
SPEAKER_00 |
| transcript.pyannote[118].start |
604.03784375 |
| transcript.pyannote[118].end |
604.08846875 |
| transcript.pyannote[119].speaker |
SPEAKER_01 |
| transcript.pyannote[119].start |
604.08846875 |
| transcript.pyannote[119].end |
604.24034375 |
| transcript.pyannote[120].speaker |
SPEAKER_00 |
| transcript.pyannote[120].start |
604.24034375 |
| transcript.pyannote[120].end |
607.49721875 |
| transcript.pyannote[121].speaker |
SPEAKER_00 |
| transcript.pyannote[121].start |
607.75034375 |
| transcript.pyannote[121].end |
610.53471875 |
| transcript.pyannote[122].speaker |
SPEAKER_00 |
| transcript.pyannote[122].start |
611.27721875 |
| transcript.pyannote[122].end |
617.97659375 |
| transcript.pyannote[123].speaker |
SPEAKER_00 |
| transcript.pyannote[123].start |
618.71909375 |
| transcript.pyannote[123].end |
626.98784375 |
| transcript.pyannote[124].speaker |
SPEAKER_01 |
| transcript.pyannote[124].start |
625.26659375 |
| transcript.pyannote[124].end |
625.78971875 |
| transcript.pyannote[125].speaker |
SPEAKER_01 |
| transcript.pyannote[125].start |
626.92034375 |
| transcript.pyannote[125].end |
629.04659375 |
| transcript.pyannote[126].speaker |
SPEAKER_00 |
| transcript.pyannote[126].start |
628.23659375 |
| transcript.pyannote[126].end |
628.67534375 |
| transcript.pyannote[127].speaker |
SPEAKER_01 |
| transcript.pyannote[127].start |
631.05471875 |
| transcript.pyannote[127].end |
631.51034375 |
| transcript.whisperx[0].start |
0.129 |
| transcript.whisperx[0].end |
19.167 |
| transcript.whisperx[0].text |
请洪玉祥委员主席好,有请农业部陈部长好,部长好 |
| transcript.whisperx[1].start |
23.281 |
| transcript.whisperx[1].end |
38.749 |
| transcript.whisperx[1].text |
這個我想剛剛早上很多委員都詢問過那因為我長期在這個關貿就是我們的這個關稅的貿易協定那我在院會有質詢好幾次我覺得這個ART的黑箱 |
| transcript.whisperx[2].start |
40.049 |
| transcript.whisperx[2].end |
64.37 |
| transcript.whisperx[2].text |
可能因為你們的人啊可能要去把這個整個ART裡的很詳細的這些英文的規範我相信可能這個那時候貿易代表署也可能沒有跟你們說的很清楚但是他一條一條寫在這邊除了我上次在院會講的野牛之外野牛肉不生沒有的這一塊那也去follow一下那另外的話其實這個進口的花生 |
| transcript.whisperx[3].start |
65.812 |
| transcript.whisperx[3].end |
80.47 |
| transcript.whisperx[3].text |
零關稅進口 其實它也是寫在ART裡面所以加上我們那個經濟的話已經那個WTO的優先繁衛美的時候所以這個是變成是昨天一個很大很大的議題那當然我們大概也清楚因為 |
| transcript.whisperx[4].start |
81.531 |
| transcript.whisperx[4].end |
110.142 |
| transcript.whisperx[4].text |
整個美國花生剛剛部長也說過進口的話大概差不多1%我們國內產的花生大概是75%左右當然我們會說口味不同怎麼樣子等等但是我們的還是有很多的憂心像昨天新聞在中央社他說了胡宗一次長ART實施可能估計影響國產花生二至三成 |
| transcript.whisperx[5].start |
111.656 |
| transcript.whisperx[5].end |
133.337 |
| transcript.whisperx[5].text |
這個我必須要先解釋一下我們胡市長講的兩到三年的前提是純粹用價格的角度看這個問題政府沒有任何支持的前提之下才會影響到農民但是我們農業部絕對不會說不做任何的作為所以我們有相關的作為去確保我們台灣的花生是能夠繼續往前走的 |
| transcript.whisperx[6].start |
134.005 |
| transcript.whisperx[6].end |
153.624 |
| transcript.whisperx[6].text |
明白啊 以前就沒有這個問題 你就不需要嘛 對不對但是現在是因為我們ART的這樣子的黑箱的時候你才會有這件事我順便跟委員報告因為花生其實這麼多年來我們大家包括業產地也是一樣希望能夠做一些升級的改變我覺得這是一個機會所以我們會跟地方政府一起努力 |
| transcript.whisperx[7].start |
156.467 |
| transcript.whisperx[7].end |
175.295 |
| transcript.whisperx[7].text |
這個我根據農業部最近的表現我是沒什麼信心啦從這個動物用藥一直到這個寵物的美容定型化契約我昨天也才開了記者會所以我覺得你們可能是執行力去上緊發條好不好這個我的期許因為我不曉得後面還會再冒出什麼 |
| transcript.whisperx[8].start |
176.69 |
| transcript.whisperx[8].end |
202.52 |
| transcript.whisperx[8].text |
還是會有 你相信我好了再不就好好把ART看一遍我們非常清楚的ART的內容像剛才講的野牛的部分因為野牛不是感受性動物雖然他是講的牛他不會受到BSE的傳染因為他不吃飼料所以相對的第二個我們台灣是進口肉牛不進口野牛那如果美國境內的BSE狀況改變了我們也會做邊境的傳染 |
| transcript.whisperx[9].start |
203.44 |
| transcript.whisperx[9].end |
230.813 |
| transcript.whisperx[9].text |
這個只是提醒你是我了解 謝謝委員再來的話因為我們還是預算所以我還是把你們的整個預算看了好幾應該看了很久了好幾天以上應該算好幾週了那基本上來講的話農業部我大概我這邊是提建議啦因為我覺得有些東西是都是執行力的問題我帶過這麼多的計畫也做過這麼多計畫審查委我覺得很多都執行力的問題 |
| transcript.whisperx[10].start |
232.073 |
| transcript.whisperx[10].end |
247.695 |
| transcript.whisperx[10].text |
第一個這幾個口號都沒問題啦智慧、韌性、永續、安心這個OK但是就是我還是關心就是這些執行力的問題那麼在我們整個支出來講的話當然增加了差不多40億左右 |
| transcript.whisperx[11].start |
248.973 |
| transcript.whisperx[11].end |
267.944 |
| transcript.whisperx[11].text |
那但是我們的農業特別收入基金這裡頭的話大概你的收入跟這個支出的話短處的話大概是差不多158.26那我們在跟預算中心立法院預算中心開會的時候其實我們農業特別收入基金是年年都是短處的 |
| transcript.whisperx[12].start |
269.225 |
| transcript.whisperx[12].end |
275.014 |
| transcript.whisperx[12].text |
那這一塊的話有時候有些單位他超支預算金額又並決算這個狀況還蠻嚴重的那請教部長這個地方要怎麼樣來看這件事情 |
| transcript.whisperx[13].start |
283.17 |
| transcript.whisperx[13].end |
293.28 |
| transcript.whisperx[13].text |
我第一個部分跟委員報告就是特收基金裡面一個變動最大的就是天然災害救助基金那天然災害救助基金以113年來講它只變了29億那像114年我講去年去年天然災害救助就變成85億那事實上我們只變了39億 |
| transcript.whisperx[14].start |
300.888 |
| transcript.whisperx[14].end |
316.119 |
| transcript.whisperx[14].text |
所以他這個不可預期性的部分 我們就會用超支併決算或者是爭取特別預算來處理那第二個部分就是基金有個特色 基金很多東西在處理食物面的部分包括農發基金 農署基金所以在整個設計的過程中 當然我們會盡量 |
| transcript.whisperx[15].start |
317.26 |
| transcript.whisperx[15].end |
338.611 |
| transcript.whisperx[15].text |
去編由國戶撥補的部分不夠的時候我們就用超知病決算那我們盡量避免這樣就是說因為我覺得我們在做任何政策的時候都是靠數據所以它也不是連年氣候的變遷這個聲音現象或什麼我想農業部對不對跟環境部這些天氣的這些的控制雖然天氣很難預控但是我們現在 |
| transcript.whisperx[16].start |
340.012 |
| transcript.whisperx[16].end |
356.746 |
| transcript.whisperx[16].text |
在這些AI的模型上還是可以把這個誤差的比例稍微降到最低因為不然這個在我們企業來講這個看起來就是很奇怪對 這個我們會來努力好不好好 提醒我現在都是提醒是 我們會來努力另外的話就是在我們的預算處有很多數位AI |
| transcript.whisperx[17].start |
357.967 |
| transcript.whisperx[17].end |
371.603 |
| transcript.whisperx[17].text |
相關的東西如果看你們今天的報告的第14頁這裡頭這裡頭就包含在科技發展支出的一個編列這邊大概就已經是有增加了雖然不多但是也算多了 |
| transcript.whisperx[18].start |
372.728 |
| transcript.whisperx[18].end |
392.424 |
| transcript.whisperx[18].text |
那整個裡頭這個項目有農業電子化然後的話呢也有跨領域整合型科技研發還有農業智慧化那我其中對那種比較大向因為你的電子化這到底應該之前就應該做啦不然你怎麼會有智慧化AI化那那個編的錢也不少大概差不多是 |
| transcript.whisperx[19].start |
394.046 |
| transcript.whisperx[19].end |
406.777 |
| transcript.whisperx[19].text |
3億 比去年再增加1.75億然後你還有農業智慧化所以我想說這些名詞在我們這些念資訊的看起來就覺得非常的confuse就到底這是什麼東西 |
| transcript.whisperx[20].start |
408.119 |
| transcript.whisperx[20].end |
436.109 |
| transcript.whisperx[20].text |
而且簡單的跟委員說明電子化智慧化它有不同的層次我想你非常清楚電子化在我們做智慧化的過程中不可能瞬間把所有的資料所有的作為全部把電子化變成智慧化我們電子化做幾年了我想電子化至少也超過十年以上有所以這裡頭的話我就覺得說一個假設我一個企業做電子化做了十年那我真的要拐門 |
| transcript.whisperx[21].start |
437.096 |
| transcript.whisperx[21].end |
441.683 |
| transcript.whisperx[21].text |
不過我想農業有它的複雜性那相對的在於所以我們半導體產業不複雜 |
| transcript.whisperx[22].start |
443.868 |
| transcript.whisperx[22].end |
468.914 |
| transcript.whisperx[22].text |
我想以產品的類別來講當然它的製程非常複雜但是以農業的議題來講它是非常非常多元化的啦沒問題啦我現在都是提醒因為我非常想要貢獻我的能量在這一塊地方因為整個東西的話你的電子化出來這些數據的話因為在審計部以及我們過去歷年在看到的東西其實有很大的問題就是 |
| transcript.whisperx[23].start |
470.834 |
| transcript.whisperx[23].end |
498.678 |
| transcript.whisperx[23].text |
你們都搞一大堆的設備然後都是分區分區這樣進行上次我也質詢過了這些資料的格式怎麼去統一然後怎麼到中台對不對然後你的AI模型到底在什麼場合用什麼東西它那個是有vertical domain的這個模型然後再上去要是解決你的應用所以我不太希望看到說我裝了多少多少人用那個對我都沒有用也就是說你的KPI要重新去define |
| transcript.whisperx[24].start |
499.338 |
| transcript.whisperx[24].end |
517.919 |
| transcript.whisperx[24].text |
從一個部長的高度去說我用了這一個東西我到底真的幫農民的比如說農損率節損了多少農業生產力提損了多少或者是我在整個這些不同的地區的農作物的栽種對不對或者是這些遙控就是這種土地測量的面積的精準程度 |
| transcript.whisperx[25].start |
519.099 |
| transcript.whisperx[25].end |
547.214 |
| transcript.whisperx[25].text |
這些東西的話把它變成是一個比較有效率的KPI比較目標導向一點就是我們不要是只有Output就是希望有Outcome跟有Impact也就是說因為經過我農業部科技化智慧化的努力我們台灣農民的生產力價值以及輸出這些是可以提升的這才是我們使用科技跟AI的目的所以我是希望把這整個 |
| transcript.whisperx[26].start |
548.479 |
| transcript.whisperx[26].end |
566.133 |
| transcript.whisperx[26].text |
有機會的話我也歡迎部長或誰帶起來做整個整體的DI的報告我非常認同委員所報告的就是我們的KPI必須要去檢討我也會重新檢討那特別著重在我們的Outcome跟Impact的部分那我想 |
| transcript.whisperx[27].start |
566.633 |
| transcript.whisperx[27].end |
584.74 |
| transcript.whisperx[27].text |
如果委員有時間 我也很樂意邀請您到我們的主管匯報來做一些專題報告讓我們的同仁有更好的認知你們報告啦 不要叫我報告啦你們報告我來給你建議這樣子好不好你要派功課給我個麻煩啊好 那後續還有很多預算的問題我想我們可以到預算的時間在這邊弄因為我覺得我為什麼非常這樣關心因為 |
| transcript.whisperx[28].start |
591.028 |
| transcript.whisperx[28].end |
609.989 |
| transcript.whisperx[28].text |
幫我主持再給我30秒就好在台灣的這個AI在發展的時候其實最有價值的一定在垂直領域而垂直領域的話我們製造業一定是一流的第二個就是我們的health care因為我們的臨床醫療第三個就別人也拿不走的就是農業 |
| transcript.whisperx[29].start |
611.531 |
| transcript.whisperx[29].end |
627.698 |
| transcript.whisperx[29].text |
我們這裡頭有很多農業的相關的不管是物種 科技就是養殖這些我覺得它是一個非常好的這個vertical domain的一個AI我們大概希望可以從這個方向來努力讓我們的農業更好照顧我們這些弱勢的農民我們非常謝謝 謝謝委員的建議謝謝 |