본 연구는 우리나라 소나무림을 대상으로 지위지수를 기반으로 한 잠재수확량을 예측하여 직관적인 임분 생산력 지표를 제시하고자 하였다. 분석 자료는 2016~2019년에 실시한 제7차 국가산림자원조사(National Forest Inventory: NFI)와 2016~2017년에 실시한 전국 소나무 실태조사 (Korea forest service, 2016; Korea forest service, 2017)를 통해 획득된 총 1,610 표본점 조사자료를 이용하였다. 지위지수 분류곡선과 임분밀도관 리도를 이용하여 각 표본점의 지위 추정 및 기준임령 60년에 대한 생장예측을 실시하여 수확이 예상되는 평균 흉고직경과 임분재적을 추정하였다. 분석 결과, 예측된 재적 수확량은 전체 표본점 중 80.9%가 150~300m3 ha-1의 재적 수확이 가능한 것으로 분석되었고, 300m3 ha-1이상의 재적을 수확할 수 있는 표본점은 223개소(13.9%)에 불과하였다. 수확이 예상되는 평균 흉고직경 예측값은 평균 흉고직경 30cm 이상의 목재를 수확할 수 있는 표본점은 전체 20.9%에 불과하였고, 절반 이상인 55.4%가 20~30cm급의 목재 수확이 가능한 것으로 확인되어 현실림의 생장 특성을 반영한 시업체계의 개선이 필요할 것으로 판단되었다. 또한 현장에서 지위지수에 따라 잠재수확량을 가늠할 수 있도록 지위지수별 잠재수확량 표를 작성하여 제공하였다. 본 연구의 결과는 앞으로 조림적지 평가 및 조림지 경영계획 수립에 활용성이 높을 것으로 사료된다.

The present study hypothesized that ratio between carcass traits components could be applied for the understanding of yield index in Hanwoo steer. A thousand data was generated based on average carcass weight (CW), loin area (LA) and backfat thickness (BT) of Hanwoo steer in December 2018 for analysis 1. Then yield index (YI) was calculated using newly established yield index equation. The correlation between yield index and each carcass traits was visualized. In the interaction between carcass traits components (LA, CW, BT) and YI, only the interactions including BT showed a regular pattern to YI. Then changes of YI according to ratio of carcass traits components were investigated. The observed interactions between LABT and CWBT were similar with Monod equation model. The changes of YI to LABT and CWBT were fitted to Monod equation, and yield constants (K1 for LABT; K2, CWBT) of each equation were calculated as 0.47 and 2.20, respectively. Carcass traits from 5 commercial Hanwoo steer farm were then employed in the second analysis. Yield constants of each farm were estimated. In estimation, R2 value for K1 (LABT) showed greater than the K2 (CWBT). Finally, each farm was plotted based on their K1 and K2 values and it was found that greater yield index of Hanwoo steer was found as increased K1 and K2. As conclusion, the present study suggested the possibility of K1 and K2 values for understanding of yield grade equation and their application in the evaluation of new model for yield grade estimation and feeding strategy.

본 연구에서는 미공병단(1975)에서 제안한 경험적 지표인 용수부족지표(Shortage Index, SI)의 기본 개념을 근거로 국내에 적용 가능한 용수부족지표를 개발하고자 한다. SI는 용수공급 부족에 의한 피해규모를 추정할 수 있는 경우, 실제 피해 정도를 반영하여 용수부족 발생빈도, 지속기간 및 부족량에 대한 용수공급능력을 종합적으로 평가할 수 있다는 특징이 있다. 먼저, 용수부족지표의 공학적 특성을 분석하고, 지표를 구성하는 두 매개변수(k, 임계값)를 추정하는 방법을 제시하였다. 이를 근거로 1995년에 해당하는 자료들에 대한 수도산업의 총 파급효과(산업연관분석 결과)와 수자원 공급 비용 간의 관계로부터 국내에 적용 가능한 용수부족지표를 개발하였으며 낙동강 수계 내 5개의 다목적댐(안동댐, 합천댐, 임하댐, 남강댐, 밀양댐)을 대상으로 그 적용성을 평가하였다. 그 결과를 정리하면 다음과 같다. 1. 본 연구에서 고려한 용수부족지표(SI)는 수자원 부족으로 인한 사회·경제적 손실비용과 수자원 공급에 필요한 비용 특성을 반영하게 되며 SI가 1보다 크게 되면 용수공급 부족으로 인한 추가적인 수자원 개발이 필요하게 된다. 2. SI의 두 매개변수(k, 임계값) 중 k는 용수공급 부족으로 인한 사회·경제적 피해의 정도, 임계값은 수자원 부족률의 특성을 나타내는데 본 연구에서는 수도산업의 총 파급효과(산업연관분석 결과)와 수자원 공급비용 간의 관계로부터 국내에 적용 가능한 용수부족지표의 매개변수를 추정하였으며 k는 2, 임계값은 7.5%가 적절한 것으로 나타났다(미국의 경우, k=2, 임계값=10%). 3. 낙동강 수계에 대한 적용 결과, 1994-1995년 가뭄발생시 합천댐과 임하댐의 용수부족지표가 1보다 크게 나타났으며 용수공급능력 문제로 인한 추가적인 수자원 공급이 필요한 것으로 나타났다.

A split-plot designed experiment including four rice varieties and 10 nitrogen levels was conducted in 2003 at the Experimental Farm of Seoul National University, Suwon, Korea. Before heading, hyperspectral canopy reflectance (300-1100nm with 1.55nm step) and nine crop variables such as shoot fresh weight (SFW), leaf area index, leaf dry weight, shoot dry weight, leaf N concentration, shoot N concentration, leaf N density, shoot N density and N nutrition index were measured at 54 and 72 days after transplanting. Grain yield, total number of spikelets, number of filled spikelets and 1000-grain weight were measured at harvest. 14,635 narrow-band NDVIs as combinations of reflectances at wavelength ~lambdal~;and~;~lambda2 were correlated to the nine crop variables. One NDVI with the highest correlation coefficient with a given crop variable was selected as the NDVI of the best fit for this crop variable. As expected, models to predict crop variables before heading using the NDVI of the best fit had higher r2 (>10~%) than those using common broad- band NDVI red or NDVI green. The models with the narrow-band NDVI of the best fit overcame broad- band NDVI saturation at high LAI values as frequently reported. Models using NDVIs of the best fit at booting showed higher predictive capacity for yield and yield component than models using crop variables.

Planting date of soybeans [Glycine max (L.) Merr.] is one of production components in cultural systems. The objective of the current study was to identify the components of soybean production and cultural practices encompassing planting dates and cultivars that respond to dry matter accumulation, harvest index and yield components. Three determinate soybean cultivars were planted on May 13 (early), June 3 (mid), and June 24 (late). Planting density was 60~times 15cm with 2 seeds (222,000 plants per ha). Soybean plants were sampled every 10 days interval from the growth stages of V5 to R8 and separated into leaves including petioles, stems, pods, and seeds. Dry matter accumulations, harvest indices, and yield components were measured. Early planting had taken 55 days from VE to R2 and late planting taken 39 days indicating reduced vegetative growth. Early planting showed higher leaf, stem, pod and seed dry weights than late planting. However, late planting appeared to be higher harvest index and harvesting rate. Vegetative mass including leaf and stem increased to a maximum around R4/R5 and total dry weight increased to a maximum around R5/R6 and then declined slightly at R8. The highest seed yield was obtained with mid planting and no difference was found between early and late plantings. Cultivar differences were found among planting dates on growth characteristics and yield components. The results of this experiment indicated that soybean yield in relation to planting dates examined was mainly associated with harvest index and harvesting rate, and planting date of cultivars would be considered soybean plants to reach the growth stage of R4/R5 after mid August for adequate seed yield.

This experiment was conducted to investigate the changes of harvest index and the relationship between harvest index and yield determination factors by different planting times in the determinate soybean cultivars, Shinpaldal and Danbaeg. Optimum planting were 23 May in 1995 and 1996. Late planting were 13 June in 1995 and 6 June in 1996. Growth period from planting to physiological maturity (R7) was shortened as planting time was delayed in two cultivars due to shortening of reproductive growth period in Shinpaldal, and of vegetative growth period in Danbaeg. Stem weight was distinctly decreased in late planting compared to optimum planting, but seed weight of both cultivars was not different between planting times. Also, seed number per pod and harvest index were significantly increased in late planting and the high correlation was found between two factors. It was suggested that increase of harvest index in late planting would be related with high assimilate use efficiency due to increase of sink capacity. The results of correlation and principal component analysis for yield determination factors showed that main factor on yield determination was pod number per plant at R5 stage associated with dry matter accumulation during early reproductive growth period, seed number per pod and harvest index were the second factor, and one hundred seed weight was the third factor. The result of this experiment indicated that yield determination in soy-bean was dependent mainly on pod number per plant related to dry matter accumulation by early reproductive growth period, and the increase of seed number per pod and harvest index could compensate for yield decrease by shortening of vegetative growth period in late planting. Such result suggests that optimum planting date can be delayed from mid May to early June in improved soybean cultivars in Korea