Green Canopy Cover Research Articles

Influence of red–blue light ratio on the phenology and morphology of different lentil (Lens culinaris Medik) and chickpea (Cicer arietinum) genotypes under a simple and resource‐efficient in‐house speed breeding method based on the application of extended photoperiod

AbstractIn the complex context of rising food demand, natural resources degradation and worsening climate changes impact, food legumes could play an important role in sustainable agriculture and food security. To overcome these challenges, enhanced genetic gain has become a necessity. Speed breeding‐based extended photoperiod is being used to reinforce conventional breeding methods and to speed up the development of adapted varieties. Speed breeding growth chambers and greenhouses' light quality in terms of red–blue ratio is among important factors that need to be optimized to enhance the efficiency of this technique to accelerate plant growth and development while limiting plant stress. We have compared the effect of different light ratios of 5:3 red–blue, 3:5 red–blue and 4:4 white–white on phenological and morphological characteristics of three lentil and chickpea genotypes grown in a speed breeding‐based extended photoperiod growth chamber with 22 h light/2 h dark. The 5:3 red–blue light ratio increased plant height and reduced flowering time for the three tested lentil varieties with 36, 41 and 40 days after sowing, respectively, as well as for the three tested chickpea varieties with 42, 38 and 24 days after sowing, respectively. While the 3:5 red–blue ratio increased leaves and secondary stem growth for lentil; leaves and secondary stem growth, green canopy cover and seedling vigour for chickpea. Less vegetative growth and later flowering were observed under white light. Higher red–blue light ratio of 5:3 accelerated lentil and chickpea vegetative growth and flowering, making it more optimal for speed breeding‐based extended photoperiod. It should be pointed out that the suggested home‐designed and built protocol is cost‐effective and easy to implement especially in small‐scale breeding programmes in developing countries.

Understanding the impact of urban heat islands on crime: insights from temperature, population density, and green canopy cover

Emergent literature suggests that micro- and macro-climates influence criminal behaviour; a complex phenomenon that is still incipient in theory development. This mixed-method research starts with a systematic review of the literature on the theoretical premises that urban heat islands amplify aggressive behaviour and crime. Further, it discusses the potential implications of the relationship between the environment and social outcomes on the design and planning of urban environments. A meta-synthesis was conducted to explain the correlations between patterns of criminal behaviour and thermal (dis)comfort. This correlation is to relate fundamental urban design principles to socially sustainable communities that dissuade violence and crime, and otherwise show poorly designed spaces do propagate criminal behaviour. Cross-validation was undertaken using a case study of Midland, a suburb of Perth, Western Australia. Data involves population demographics, temperature, and crime statistics relating to Offences against the Person. Analysis focused on homicide, assault, threatening behaviour, and robbery. The findings imply a positive correlation between long-term temperature, crime, population density, and green canopy cover. Variables of climate (e.g., short-to-long-term climate-related stressors) and crime types also show non-linear association. Nonetheless, forecasting the future of violence and trends of crime through attributes or potential impacts of heat and urban canopy cover on the built environment will inform sustainable social development policy, environmental planning, development strategy, designers, and planners. Recommendations are made around these in relation to making urban communities adaptive to the impacts of global warming and future densification.

Exploring the impact of light intensity under speed breeding conditions on the development and growth of lentil and chickpea

The use of high-performant varieties could help to improve the production of food legumes and thus meet the demand of the growing world population. However, long periods needed to develop new varieties through traditional breeding are a major obstacle. Thus, new techniques allowing faster genetic advance are needed. Speed breeding using longer periods of light exposure on plants, appears to be a good solution for accelerating plant life cycles and generation turnover. However, applying extended photoperiod causes plant stress and mortality due to lack of information on the adequate intensity to be used in speed breeding protocol. This study examines the impact of light intensity under speed breeding conditions on the development and growth of lentils and chickpeas, with a keen interest in enhancing genetic gain in these key food legumes. Four distinct levels of light intensity (T1: Green-house: 2000 µmol/m2/s; T2: 148–167 µmol/m2/s; T3: 111–129 µmol/m2/s; T4: 74–93 µmol/m2/s) under a photoperiod of 18 h of light and 6 h of darkness were tested in a growth chamber. Significant variation depending on light intensity was observed for plant height, total biomass, number of secondary stems, pods number, number of seeds per plant, growth rate, green canopy cover, time to flowering, time of pod set, time to maturity, vegetative stage length, reproduction stage length and seed filling stage length. Light intensity significantly influenced flowering/maturity and plant’s stress compared to normal conditions in green-house where flowering/maturity were around 67/97 days for lentil and 79/111 days for chickpea. Therefore, lentils in treatment 2 flowered and reached maturity in 30/45 days respectively, with high stress, while chickpeas in the same treatment did not flower. In contrast, treatment 4 showed interesting results, promoting optimal growth with low stress, and flowering/maturity in 27/46 days and 28/54 days, respectively for lentils and chickpeas. These results underline the crucial importance of light management in speed breeding to accelerate vegetative growth and phenology while allowing healthy growth conditions for plants to produce enough seeds for generation turnover.

Exploring genetic variability under extended photoperiod in lentil (Lens Culinaris Medik): vegetative and phenological differentiation according to genetic material’s origins

Lentil is an important pulse that contributes to global food security and the sustainability of farming systems. Hence, it is important to increase the production of this crop, especially in the context of climate changes through plant breeding aiming at the development of high-yielding and climate-smart cultivars. However, conventional plant breeding approaches are time and resources consuming. Thus, speed breeding techniques enabling rapid generation turnover could help to accelerate the development of new varieties. The application of extended photoperiod prolonging the duration of the plant’s exposure to light and shortening the duration of the dark phase is among the simplest speed breeding techniques. In this study, genetic variability response under extended photoperiod (22 h of light/2 h of dark at 25 °C) of a lentil collection of 80 landraces from diverse latitudinal origins low (0°–20°), medium (21°–40°) and high (41°–60°), was investigated. Significant genetic variations were observed between accessions, for time to flowering [40 → 120 days], time of pods set [45 → 130 days], time to maturity [64 → 150 days], harvest index [0 → 0.24], green canopy cover [0.39 → 5.62], seedling vigor [2 → 5], vegetative stage length [40 → 120 days], reproduction stage length [3 → 13 days], and seed filing stage length [6 → 25 days]. Overall, the accessions from Low latitudinal origin demonstrated a favorable response to the extended photoperiod application with almost all accessions flowered, while 18% and 57% of accessions originating from medium and high latitudinal areas, respectively, did not successfully reach the flowering stage. These results enhanced our understanding lentil responses to photoperiodism under controlled conditions and are expected to play important roles in speed breeding based on the application of the described protocol for lentil breeding programs in terms of choosing appropriate initial treatments such as vernalization depending on the origin of accession.

Soil texture, fertilization, cover crop species and management affect nitrous oxide emissions from no-till cropland

Cover crops reduce nitrate leached, but effects on nitrous oxide (N2O) emissions are mixed. Cover crops can reduce N2O emissions by reducing levels of mineral nitrogen (N) and surface soil moisture during spring. Cover crops can also increase N2O emissions by adding organic substrates, releasing N during decomposition, or increasing summer soil water content. Winter-killed cover crops can increase soluble organic C and N during periods of typically low microbial activity. We hypothesized that planting a cover crop mix of radish (Raphanus sativus)-crimson clover (Trifolium incarnatum)-rye (Secale cereale) would increase direct N2O emissions relative to no cover crop, and result in lower direct and indirect N2O emissions than planting radish alone. We also hypothesized that extending the cover crop growing season, by planting earlier and killing later, would increase direct N2O emissions during winter, decrease direct N2O emissions during summer, and decrease indirect N2O emissions. To address these hypotheses, we conducted two field experiments (on sandy and silty soils) over four site-years. We measured cover crop biomass and N content, soil mineral N concentrations, soil moisture, green canopy cover, soil porewater nitrate, direct N2O emissions, and estimated indirect N2O emissions. Nitrous oxide emissions were ~ 7.8 times greater at the silty than the sandy sites due to greater soil moisture retention. Site-years with high radish biomass exhibited greater direct N2O emissions than sites with low radish biomass following winter-kill. Indirect N2O emissions were decreased ~7 % by planting cover crops and by ~70 % by planting cover crops early. Fertilizer induced emission peaks were 8.2 times greater than all previous N2O emissions combined at a silty site. Our results suggested that soil texture and fertilization played an important role in direct N2O emissions, while cover crop species, biomass, and timing played a more important role in NO3 leached, and thus, indirect N2O emissions.

Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI

Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. However, analysis of the potential of these techniques to improve water management in irrigated potato (Solanum tuberosum L.) is still lacking. This study aimed to assess the modified nonlinear relationship between LAI, Kcb and NDVI in estimating crop coefficients (Kc) and ET of potato. Moreover, Kc and ET were derived from the measured fraction of green canopy cover (FGCC) and the FAO-56 approach. ET estimated from the FAO-56, FGCC and NDVI approaches were compared with the ET simulated using the LINTUL-Potato model. The results showed that the Kc values based on FGCC and NDVI were on average 0.16 lower than values based on FAO-56 Kc during the mid-season growing stage. ET estimated from FAO-56, FGCC and NDVI compared well with ET calculated by the LINTUL-Potato model, with RMSE values of 0.83, 0.79, and 0.78 mm day−1, respectively. These results indicate that dynamic crop coefficients and potato ET can be estimated from canopy cover and NDVI. The outcomes of this study will assist potato growers in determining crop water requirements using real-time ETo, canopy state variables and NDVI data from satellite images.

A simplified approach to satellite-based monitoring system of sugarcane plantation to manage yield decline at Wonji-Shoa Sugar Estate, central Ethiopia

Drastic and continuous decline in cane yields has become a major threat to sustainable sugarcane production in Ethiopia. Among the causes for the decline are the inefficient and ineffective system of monitoring sugarcane plantations. Adopting satellite-based crop monitoring through the Landviewer platform may circumvent this problem. However, the reliability of vegetation indexes calculated by the platform is unknown and thus requires evaluation. Accordingly, we tested the accuracy of selected Landviewer Calculated Vegetation Indexes (LCVIs) on three major sugarcane varieties and two cropping types. The goodness-of-fit of the sigmoid curve to the LCVIs profile of sugarcane was evaluated. The correlations between LCVIs and yield components, LCVIs and fractional green canopy cover (FGCC), as well as the time-serious Normalized Difference Vegetation Index (NDVI) and yields, were also analysed. We found that the goodness-of-fit of the sigmoid curve was significant (p < 0.001), with 84%–95% accuracy in all the indexes. The majority of LCVIs showed significant (p < 0.05) relationships with yield components and FGCC. The time-series NDVI also demonstrated a significant relationship with cane yield (R2 = 0.73–0.85) at the age of 10 months and above. The accuracy level of LCVIs varies with varieties and crop types, but the Normalized Difference Phenology Index (NDPI), Soil Adjusted Vegetation Index (SAVI), and NDVI were identified as the most consistent and effective LCVIs for sugarcane monitoring. Therefore, the accuracy of LCVIs was dependable and can be used effectively in monitoring sugarcane plantations to tackle the problem of continuous decline in the yield of the crop.

Leaf, Root, and Crown Tissue Physiology of Annual Bluegrass after Cold Acclimation at Varying Soil Moisture Levels and Ice Encasement

Annual bluegrass (Poa annua) is a turfgrass species prone to winterkill-induced damage such as from ice encasement stress. This research aimed to determine whether different levels of soil volumetric water content (SWC) influence cold acclimation and recovery from ice encasement. Annual bluegrass was exposed to 8%, 12%, and 20% SWC treatments during cold acclimation in growth chambers. After cold acclimation, plants were subjected to ice encasement by misting at –3 °C until a 2.5-cm ice layer was formed. On 0 (no ice encasement exposure), 40, and 80 days of treatment, plants were analyzed for recovery (percent green canopy cover over time), and leaf, crown, and root tissues were harvested for lipid peroxidation and total nonstructural carbohydrates (TNC) including storage carbohydrates and water-soluble carbohydrates (WSC). Low SWC during cold acclimation enhanced recovery from cold temperatures and ice encasement. Root carbohydrates were influenced by SWC regimes during cold acclimation since day 0 plant roots exposed to the 8% SWC treatment had 143.9% higher TNC and 137.6% higher WSC compared with day 0 plants exposed to 12% and 20% SWC. Root levels of carbohydrates and lipid peroxidation were least influenced by cold and ice encasement among the organs evaluated. Prolonged freezing conditions and ice encasement reduced leaf and crown tissue carbohydrates and increased lipid peroxidation compared with day 0 plants not exposed to freezing temperatures and ice encasement. After 40 days of ice encasement, plants exposed to the 8% SWC treatment recovered faster than plants cold acclimated at higher soil moisture levels. Average percent canopy cover after 36 days of recovery in the greenhouse was 71.9% higher for 8% SWC treated plants than in 12% and 20% SWC treated plants. Turfgrass managers may benefit from annual bluegrass putting green management strategies to reduce fall soil moisture. Given that soil moisture did not significantly influence carbohydrate or lipid peroxidation results, except for in roots, additional research is needed to understand the mechanism associated with these findings.

Cannabidiol industrial hemp growth, biomass, and temporal cannabinoids accumulation under different planting dates in southern Florida

AbstractDue to limited information, identifying suitable cannabidiol (CBD) dominant industrial hemp (Cannabis sativa L.) cultivars and optimal planting date are critical for hemp commercialization for CBD production in Florida. Two field trials were conducted with seven cultivars planted on three different dates from late April to late June. The cultivars were received from two different suppliers (Kentucky and Colorado) representing their adaptation. Plant growth parameters such as plant height and green canopy cover were recorded at 2−3 weeks intervals during crop growth. Temporal CBD and Δ‐9‐tetrahydrocannabinol (THC) concentrations in developing flowers were analyzed weekly beginning two weeks after flowering until maturity. The final floral biomass yield was measured at harvest. CBD and THC concentration curve over crop growing season varied among the tested cultivars. High positive correlations between CBD and THC concentrations (R2 = 0.88−0.98) were observed in all cultivars. This resulted in similar CBD/THC ratios across all planting conditions. Earlier planting resulted in higher CBD, THC, and floral biomass yield in a few cultivars, with no significant effect in most cultivars. Cultivars adapted in Kentucky performed better compared to Colorado in floral biomass and CBD yield. However, THC concentrations in the cultivars adapted in Kentucky mostly exceeded the acceptable legal threshold (0.3% THC) at harvest. Results indicate cultivar performance was affected more by genetics compared to planting date. Screening more cultivars adapted to regions of similar latitudes and environmental conditions is necessary to identify suitable hemp cultivars for Florida or similar climatic conditions.

Study of the Relationship between Biomass and Fractional Green Canopy Cover of Two Forage Crops Using Canopeo®

Rapid and accurate estimation of biomass is crucial for the assessment of the crop nutrition status and the improvement of crop management strategies. The study was conducted in Setif province (Northeast of Algeria). Canopy cover was calculated using Canopeo® application, at stem elongation, booting, and heading stages for triticale, and V5, R1, and R3 for lentil. The results indicate a high correlation between canopy cover and triticale biomass, at the stem elongation stage with R2 = 0.98 and 0.89 under no till and conventional till, respectively. The RMSE was 0.1 and 0.06t/ha, under the two modes. The lowest correlation is seen at heading stage with a value of 0.21 and RMSE of 0.65t/ha, under no till mode. For lentil, the high correlations between are observed at R3 (early pod), and R1 (early bloom) stages, with a coefficients of 0.85 and 0.98, under zero till and conventional respectively. The RMSE was 0.17t/ha. This smartphone application is suitable for assessing triticale and lentil biomass; it is rapid, easy, and could replace the destructive sampling method.

EVALUATING URBAN GREENING SCENARIOS ON URBAN HEAT ISLANDS EFFECTS

An Urban Heat Islands (UHI) are one of the most common phenomena of the 21st century. Climate change, and the associated rising trend of urban temperature is exaggerating the creation and extension of urban heat islands (UHI). This paper investigates various urban greening scenarios in the line with developing the resilient Sydney strategical document. Two different cooling street strategies are investigated in this research including high reflective pavement and green canopy cover. ENVI-Met software has been applied for simulating and quantifying microclimate processes at the urban scale before and after introducing urban greening scenarios. The results of this study show that applying the light-coloured pavement instead of asphalt surface will decrease surface temperature by up to 20°C. Planting short to medium height trees in the side walks will decrease the air temperature by up to 3°C and surface temperature by up to 11°C.

Yield estimations in a vineyard based on high-resolution spatial imagery acquired by a UAV

Yield estimation in vineyards is an essential issue for performing cropping practices to reach the desired production quality or quantity. Sporadic manual measurements have been traditionally made. In the last decade, vegetation indices (VIs) and geometric parameters obtained from unmanned aerial vehicle (UAV) imagery have been related to different vine biophysical features. This research aimed to evaluate the potential of VIs and green canopy cover (GCC; as a measure of plant vigour) obtained from conventional (or red, green, blue; RGB) and multispectral sensors that monitor spatial intraplot variability for yield predictions. The yield components traditionally sampled in an early growth cycle stage (pea berry size) were combined with UAV imagery-based products. The proposed methodology was applied to a vineyard in southeastern Spain during the 2019 and 2020 growing seasons. Rain-fed and irrigated treatments were implemented. Flights were performed throughout the growth cycle using RGB and multispectral cameras mounted on a UAV. Orthoimages were generated. Computer vision techniques were used to segment these orthoimages to obtain vegetation-only masks. Simple and multiple linear regression techniques were evaluated by using VIs alone, VIs combined with GCC and yield components as predictors. The RMSE values ranged from 0.21 kg vine −1 to 0.39 kg vine −1 when yield components, RGB or multispectral VIs were employed. Therefore, with all the advantages that their use entails, RGB and multispectral sensors are a good option for estimating the final yield of vineyards despite calibration being required for each season and grapevine plot. • An approach was proposed to estimate yield in vineyards with UAVs. • Multispectral and RGB VIs and grapevine vigour were employed. • The method used yield components sampled in early growth cycle stages. • Estimations were made with small errors combining UAV data with yield components.

Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water Status

The development of unmanned aerial vehicles (UAVs) and light sensors has required new approaches for high-resolution remote sensing applications. High spatial and temporal resolution spectral data acquired by multispectral and conventional cameras (or red, green, blue (RGB) sensors) onboard UAVs can be useful for plant water status determination and, as a consequence, for irrigation management. A study in a vineyard located in south-eastern Spain was carried out during the 2018, 2019, and 2020 seasons to assess the potential uses of these techniques. Different water qualities and irrigation application start throughout the growth cycle were imposed. Flights with RGB and multispectral cameras mounted on a UAV were performed throughout the growth cycle, and orthoimages were generated. These orthoimages were segmented to include only vegetation and calculate the green canopy cover (GCC). The stem water potential was measured, and the water stress integral (Sψ) was obtained during each irrigation season. Multiple linear regression techniques and artificial neural networks (ANNs) models with multispectral and RGB bands, as well as GCC, as inputs, were trained and tested to simulate the Sψ. The results showed that the information in the visible domain was highly related to the Sψ in the 2018 season. For all the other years and combinations of years, multispectral ANNs performed slightly better. Differences in the spatial resolution and radiometric quality of the RGB and multispectral geomatic products explain the good model performances with each type of data. Additionally, RGB cameras cost less and are easier to use than multispectral cameras, and RGB images are simpler to process than multispectral images. Therefore, RGB sensors are a good option for use in predicting entire vineyard water status. In any case, field punctual measurements are still required to generate a general model to estimate the water status in any season and vineyard.

ІНФОРМАЦІЙНО-ДИНАМІЧНА МОДЕЛЬ УПРАВЛІННЯ СЕРВІСНИМ ВІДНОВЛЕННЯМ ПРАЦЕЗДАТНОСТІ ЗЕРНОЗБИРАЛЬНИХ КОМБАЙНІВ

The article presents the results of a study on the relationship and the possibility of mutual conversion between the normalized differentiated vegetation index (NDVI) and fractional green canopy cover (FGCC) in legumes. The purpose of the work is to provide proposals for models of mutual conversion between the vegetation indices. Materials and methods. The basis of the study was formed by the photographic materials of the leguminous crops, taken at different phases of growth and development on the fixed sites every 10 m of the experimental fields, obtained and automatically calculated to establish the percentage of green canopy cover in Canopeo mobile application. Experimental materials were obtained in 2021 in the experimental fields of the Institute of Irrigated Agriculture of NAAS and in 2016 in the experimental field of the AC "Radyanska Zemlya". Subsequently, the geotags and dates of shooting photographic materials were tied to the satellite NDVI values ​​obtained through the OneSoil AI platform for each of the studied crops (the difference in dates is up to 3 days). The established pairs of FGCC and NDVI for each crop were submitted for statistical analysis by polynomial regression. Statistical calculations and graphical work were performed in Microsoft Excel 365 spreadsheets. Results. It has been established that there is a close linear and nonlinear relationship between the FGCC and NDVI indices of the studied crops. The strength of the relationship is high, with a correlation coefficient in the range of 0.95-0.98 and a determination coefficient of 0.90-0.97. Polynomials of the second stage (nonlinear regression models) can be successfully used for mutual conversion between vegetation indices; the error in recalculation for most crops did not exceed 10 %. Conclusions. A high affinity between the normalized differential vegetation index and the percentage of green canopy cover in the leguminous crops (R2 within 0.90-0.97) has been proven. The closest relationship is observed for soybeans and beans compared to chickpeas and peas. Key words: peas, common beans, conversion, chickpeas, regression analysis, soybean.

Winter wheat light interception measured with a quantum sensor and images

AbstractLight interception (LI) measurements are essential for modeling crop biomass and grain yield. Canopy LI is often measured with linear quantum sensors, but the need for unobstructed sunlight near solar noon can make field measurements impractical. We aimed to evaluate the use of the fraction of green canopy cover (FGCC) as a surrogate for LI in winter wheat (Triticum aestivum L.) canopies. A linear quantum sensor and a camera were used to collect 930 observations during the 2021 growing season across three locations in Kansas. Images were analyzed with the Canopeo application. The quantum sensor was also used to estimate the LI and leaf area index (LAI) in different canopy strata. Growing season shoot dry biomass was modeled with LI values from images and the quantum sensor. The LI estimated by the quantum sensor and images resulted in a strong positive linear correlation (r = 0.97). During the vegetative stages, LI values from images were generally greater than the LI values from the quantum sensor. During the reproductive stages, LI values from images were generally lower than those obtained by the quantum sensor, which was partly attributed to the better discrimination of FGCC for senescing vegetation. Using images to measure LI was easier and faster in prostrate canopies, and less sensitive to soil microrelief and changing sky conditions. The quantum sensor was useful for quantifying LI and LAI nondestructively in different canopy layers. Final growing season shoot dry biomass determined by either method was within 5% of the observed biomass.

Biodiversity Conservation of National Parks and Nature-Protected Areas in West Africa: The Case of Kainji National Park, Nigeria

Due to rising global warming and climate change, biodiversity protection has become a critical ecological concern. Rich biodiversity zones are under threat and are deteriorating, necessitating national, regional, and provincial efforts to safeguard these natural areas. The effective conservation of national parks and nature-protected areas help to improve biodiversity conservation, forest, and urban air quality. The continuous encroachment and abuse of these protected areas have degraded the ecosystem over time. While exploring the geophysical ecology and biodiversity conservation of these areas in West Africa, Kainji National Park was selected for this study because of its notable location, naturalness, rich habitat diversity, topographic uniqueness, and landmass. The conservation of national parks and nature-protected areas is a cornerstone of biodiversity conservation globally. This study is aimed at the target United Nations’ Sustainable Development Goal 13, 2030—Climate Action targeted at taking urgent action towards combating climate change and its impacts. The study captures both flora and fauna that are dominant in the study area. The 15 identified tree species were selected from over 30 species with 563,500,000 (an average of 3,700,000 in each sample frame) trees for every tree species/type with a total of 63% tree green canopy cover. The study areas divided into three zones were randomly sampled within a stratum of 25 × 25 km frames divided into 150 sample frames for proper analyses using the i-Tree Eco v6.0.25. It is a United States Department of Agriculture (USDA) Forest Service peer-reviewed application (software) designed which includes tools for urban and rural forestry study and benefits evaluations. The following microclimatic data were captured and analyzed photosynthetically active radiation, rain/precipitation, temperature, transpiration, evaporation, water intercepted by trees, runoff avoided by trees, potential evaporation by trees, and isoprene and monoterpene by trees. This study also further discusses the tree benefits of a green, low carbon, and sustainable environment within the context of biodiversity conservation, considering carbon storage, carbon sequestration, hydrology effects, pollution removal, oxygen production, and volatile organic compounds (VOCs). There is a quick need for remotely-sensed information about the national parks, protected areas and nature reserves at regular intervals, and government policies must be strict against illegal poaching, logging activities, and other hazardous human impacts.

Modulation of Wheat Yield Components in Response to Management Intensification to Reduce Yield Gaps.

Appropriate genotype selection and management can impact wheat (Triticum aestivum L.) yield in dryland environments, but their impact on yield components and their role in yield modulation are not well understood. Our objectives were to evaluate the yield response of commercial winter wheat genotypes to different management practices reflecting a stepwise increase in management intensity (including a reduction in crop density under high input), and to quantify how the different yield components modulate wheat yield. A factorial experiment evaluated six management (M) intensities [“farmer practice” (FP), “enhanced fertility” (EF), “ecological intensification” (EI), “increased foliar protection” (IFP), “water-limited yield” (Yw), and “increased plant productivity” (IPP)] and four winter wheat genotypes (G) in four Kansas environments (E). Average grain yield was 4.9 Mg ha–1 and ranged from 2.0 to 7.4 Mg ha–1, with significant two-way interactions (E × M and E × G). The EF usually maximized yields in dry environments, while EI, which consisted of EF plus one fungicide application, maximized yields in environments with greater water availability. Across all sources of variation, kernels m–2 and aboveground biomass were the strongest modulators of yield as compared to kernel weight and harvest index, while spikes m–2 and kernels spike–1 modulated yields at a similar magnitude. Foliar fungicides improved yield through increased green canopy cover duration and greater radiation intercepted during grain filling. When crop density was reduced from 2.7 to 1.1 million plants per hectare in an otherwise high-input system, plants produced more productive tillers (with genotype-specific response); however, reduced green canopy cover at anthesis and reduced cumulative solar radiation intercepted during grain filling limited wheat yield—although large differences in canopy cover or intercepted radiation were needed to cause modest changes in yield. Treatments more intensive than EI were not warranted as EF or EI maximized yields at all environments, and practices that promote biomass and kernels m–2 are to be targeted for future increases in wheat yield.

The Study on the Relationship between Normalized Difference Vegetation Index and Fractional Green Canopy Cover in Five Selected Crops.

Crop models are of great use and importance in modern agriculture. Most models imply spatial vegetation indices, such as NDVI, or canopy cover characteristics, such as FGCC, to provide estimation of crops conditions and forecast productivity. The purpose of the study was to (1) determine the possibility of mutual conversion between spatial NDVI and Canopeo-derived FGCC in five crops (grain corn, sunflower, tomato, millet, and winter wheat) and (2) estimate the precision of such a conversion. The data set of the study was formed by the OneSoil AI derived satellite imagery on NDVI for the studied crops in different stages of their growing season combined with Canopeo-processed photographs of vegetating crops in the field with FGCC percentage calculation. The sets of NDVI and FGCC values were paired up and then statistically processed to obtain polynomial equations of NDVI into FGCC and inverse conversion for each crop. The results of the study revealed that mutual conversion between spatial NDVI and Canopeo-derived FGCC is possible. There is a strong direct correlation (R2 within 0.6779–0.9000 depending on the crop) between the studied indices for all crops. Close-growing crops, especially winter wheat, showed the highest correlation, while row crops and especially tomatoes had a less strong relationship between vegetation indices. The models for mutual conversion between FGCC and NDVI could be incorporated into the yield simulation models to improve the forecasting capacities.

The comparison of Canopeo and samplepoint for measurement of green canopy cover for forage crops in India

Canopy covers can be measured using destructive (visual) and non-destructive methods (spectral indices, photogrammetry, visual assessment, and quantum sensor). The precision of crop cover estimation, however, is dependent on the selection of appropriate methods. Studies were conducted at the Indian Grassland and Fodder Research Institute, Jhansi to compare the forage crops canopy cover estimated using photogrammetry software (Canopeo and SamplePoint) and visual assessments. Assessments were performed in three summer crops (corn, cowpea, and sorghum), two winter crops (Egyptian clover, and oats), and bare ground condition. For each plot, three nadir images (directly above the canopy) were captured using digital cameras from a height of 1.5 m above the soil surface between 10 AM to 2 PM on bright sunny days. The results indicated that the relationships between visual assessment and Canopeo (regression coefficient, (R2=0.96), visual assessment and SamplePoint (0.96), and Canopeo and SamplePoint (0.98) were linear when data were pooled across all the crops. SamplePoint and Canopeo is further, appropriate for cowpea (Pearson coefficient (R=0.99 and 0.94), oats (0.92 and 0.97), and sorghum (0.46 and 0.51), respectively. SamplePoint and Canopeo are not suitable for berseem (-0.15) and corn (-0.61), respectively, due to dead residues after the first harvest in berseem and taller corn might have influenced the image quality. Therefore, the stage of the crop, the height of the crop, and dead residues around the plants can greatly influence the estimation of crop cover. In conclusion, the results indicated that this photogrammetry software can be used for non-destructive crop canopy measurement with the above-mentioned precautions in the forage crops tested. •Forage canopy cover is estimated generally by visual scoring, and the outcome varies widely from person to person.•Photogrammetry methods (Canopeo and SamplePoint) were positiviely correlated with visual scoring for cowpea, oats, and sorghum.•However, Canopeo and SamplePoint may not suitable for taller crops like corn and ratoon crops like berseem.

Predicting Winter Wheat Grain Yield Using Fractional Green Canopy Cover (FGCC)

Optical sensors have grown in popularity for estimating plant health, and they form the basis of midseason yield estimations and nitrogen (N) fertilizer recommendations, such as the Oklahoma State University (OSU) nitrogen fertilization optimization algorithm (NFOA). That algorithm uses measurements of normalized difference vegetative index (NDVI), yet not all producers have access to the sensors required to make these measurements. In contrast, most producers have access to smartphones, which can measure fractional green canopy cover (FGCC) using the Canopeo app, but the usefulness of these measurements for midseason yield estimations remains untested. Our objectives were to (1) quantify the relationship between NDVI and FGCC, (2) assess the potential for using FGCC values in place of NDVI values in the current OSU Yield Prediction Model, and (3) compare the performance of NDVI and FGCC-based yield prediction models from the collected dataset. This project, implemented on 13 winter wheat sites over the 2019-2020 growing season, used a range of nitrogen (N) rates (0, 34, 67, 101, and 134 kg N ha−1) to provide different levels of yield. Our results indicated that while NDVI and FGCC are highly correlated (r2 = 0.76), FGCC is not suitable for direct insertion into the current yield prediction model. However, a yield prediction model derived from FGCC provided similar estimates of yield compared to NDVI (Nash Sutcliffe Efficiency = −3.3). This new FGCC-based model will give more producers access to sensor-based yield prediction and N rate recommendations.