Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data

Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.

The Norwegian Forest Resource Map SR16: Mapping Forest Attributes Using Heterogeneous Sets of Airborne Laser Scanning and National Forest Inventory Data

Background The Norwegian forest resource map SR16 combines national forest inventory (NFI) and airborne laser scanning (ALS) data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Material and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and ⅔ of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) strata by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) a stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35, 34, 31, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2-7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0-4 pp smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0 – 3.9 pp, depending on the main tree species. RSMEs for timber volume ranged between 19 – 59% among individual ALS projects.Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.

Potato Varieties Response to Soil Matric Potential Based Irrigation

Potato is one of the most cropped plants worldwide. Hundreds of different varieties are cultivated only in North America. Potato growers usually crop multiple varieties on their farms to answer the market demands for potato’s specific physical properties. However, few pieces of information are available regarding the optimal management of irrigation across potato varieties. Knowing that modern potatoes share genetics similarities, the optimal irrigation comfort zone for the potato crop might be the same for different groups of varieties. This study evaluates the effect of precision irrigation thresholds on the potato yields of three varieties (Envol: very early, Kalmia: early, and Red Maria: mid-late) with different maturity classes. In a greenhouse, a soil matric potential sensor network used in combination with a precise irrigation system allows the identification of a common optimal precision irrigation threshold, allowing optimal yields for the three varieties. This paper presents the first identification of an optimal irrigation threshold, −15 kPa, shared by different potato varieties. The optimal irrigation threshold identified in this study is not dependent on the maturity class, plant height or tuber potential production. The determination of an optimal precision irrigation threshold will allow potato growers to adapt their farm management processes to integrate more sustainable water management practices as they will be able to irrigate a field with multiple varieties with the same threshold.

X-ray Technology to Determine Peanut Maturity1

ABSTRACT Indeterminate growth of peanut (Arachis hypogaea L.) creates indecision for best digging date for maturity and economic return. The current standard to determine peanut maturity is the Hull Scrape method. This method uses human observations to place hull scraped peanuts on a color maturity profile board. Human observations may lack precision and repeatability from individual to individual. X-ray technology has the capability of viewing peanut kernels through the hull to possibly ascertain density and maturity. The objective was to determine if x-ray could be used as a quick, non-destructive, and repeatable method to determine peanut maturity of runner, spanish, and virginia market types. Fresh dug peanut pods had 25 percent greater peanut area and gray scale values compared with hull scraped pods (runner and virginia only) and showed no difference in x-ray value between immature and fully mature peanut. Dried peanut showed a linear response of x-ray value versus peanut maturity (hull color). Virginia market type had much higher x-ray values followed by runners, then spanish. The relationship between peanut maturity and x-ray value peaked at the Orange class for runners (Georgia-06G, Georgia-13M), and Spanish (AT9899) while virginia (Georgia-11J) tended to peak at the Brown class. This research demonstrated that x-ray technology may be used to measure peanut density and possible maturity but needs further examination past Orange and Brown maturity class. Final x-ray values determined by this proprietary x-ray equipment may not be transferable due to specific x-ray power, detector precision, background color/scatter, and other electronic nuances.

Apricot fruit chilling injuries during the cold storage affected by harvest maturity

The study aimed to find the effect of different maturity classes (up to the days after full blossom) on the post-harvest life of 3 different apricot varieties Gold Cot, Bergarouge and Flavor Cot in Hungary. The fruit harvested in a particular time 65, 75 and 85 days after full blossom and divided to three maturity classes (Class 1, 2 and 3, respectively). Fruit stored in cold storage for 7 days at temperature 1 °C. Fruits were investigated in regard to physical parameters (weight loss, fruit firmness and soluble solid content SSC) and chilling injuries. The results showed that the varieties followed different ways in regard to response to different maturity classes. The maturity class I for all the tested varieties recorded the lowest weight loss, while with the increasing maturity stage the weight loss percentage for all the varieties recoded high values. The firmness decreased with delayed harvesting for all the varieties. Data of the chilling injuries showed that all the fruits which harvested at maturity I, recorded the highest percentage of fruit with CI (chilling injuries) at 0 (48.5, 37.25 and 38.75%) and CI class I (44.75, 35.75 and 39.75%) for Bergarouge, Gold Cot, and Flavor Cot.

Optimization of Germination Inhibitors Elimination from Sugar Beet (Beta vulgaris L.) Seeds of Different Maturity Classes

Endogenous inhibitors have a crucial effect on the success of sugar beet (Beta vulgaris) seed germination. The strength of this effect changes during seed maturation, and washing away these inhibitors can facilitate germination. Investigation of various washing factors such as water-to-seed ratio, stirring time, and speed of stirring on a mixed-maturity lot of sugar beet seeds revealed the significant effect of the interaction of all factors. Seeds with different maturity classes, from the same lot, were washed using procedures determined from the first experiment. Statistical analysis of the germination data of the washed seeds showed a significant difference in mean germination time depending on maturity class. Washing seeds of different maturity classes with 40 mL of water per gram of seed improved the germination performance of maturity classes 2 and 5, compared with 20 mL water per gram of seed. The UV absorbance of inhibitors washed from seeds differed between maturity classes. The change in seed moisture content during the washing process is another factor altered by the extent of seed development, and this variation in water uptake may reflect the pericarp structure of seed in different maturity classes. The findings in this manuscript highlight the importance of optimizing the pre-treatment procedures in accordance with the individual seed lot.

Effects of Harvest Maturity on Chilling Injury and Storage Quality of Apricots

Fresh apricots have high nutritional value and demand. Determination of the appropriate maturity is vital for fruit storage. The effects of harvest maturity on chilling injury and storage quality were investigated in this study. Xinjiang Saimaiti apricots were used as the material; the fruit was picked at three different maturity classes, maturity class I (colored area < 50%), maturity class II (colored area 50–80%), and maturity class III (colored area > 80%) according to yellow conversion rate, and stored at 0°C and 90–95% RH. Chilling injury incidence, chilling index, and the physiological indicators were evaluated. The results showed that the incidence, index of chilling injury, and firmness in apricots of maturity class I were highest than other two groups, but maturity class I apricots did not ripe. Although the incidence and index of chilling injury in maturity class III were relatively low, fruit firmness decreased rapidly. The incidence and index of chilling injury of apricots in maturity class II were lower than those of fruits at maturity class I, whereas fruit firmness, soluble solid content, ascorbic acid level, and extractable juice quantity relatively were well-maintained. Therefore, maturity class II was considered the appropriate maturity stage at harvest for storage.

Water Use Efficiency of Maize Varieties under Rain-Fed Conditions in Zambia

This study evaluated water use efficiency (WUE) of selected hybrid maize (Zea Mays L.) varieties in Zambia under rain-fed conditions. A randomized complete block field experiment was carried out during the 2014/2015 rainy season at the University of Zambia Agricultural Demonstration Centre. Treatments were 30 maize varieties from the early, medium and late maturity classes. WUE was calculated as the ratio of yield to evapotranspiration (ET) and transpiration (T). Results showed significant differences in WUE dry matter (DM) for transpiration (T) of early maturing varieties. However, no significant differences were observed in WUEDM for evapotranspiration (ET), WUE grain yield (GY), T and WUEGY, ET. WUEDM, T, WUEDM, ET, WUEGY, T, and WUEGY, ET were statistically the same among medium maturing varieties. Results further showed that among the late maturing varieties, WUEDM, T, WUEDM, ET and WUEGY, T showed significant differences but no significant differences were observed in WUEGY, ET. It was concluded that maize varieties from the same maturity classes have different WUEs. The study thus provided options in variety selection based on which varieties performed better, particularly SC 525, SC 513 and PAN 4M 21 from the early maturity class; PHB 30G19, ZMS 606, MRI 634 and SC 637 from the medium maturity class; and PAN ZM 83, SC 709, PAN 8M 93 and SC 719 from the late maturity class. It was recommended however, that repeated experiments over time should be done to validate the findings given that the trial was only conducted in one season.