“…The highest harvest forecasting power shown by f PAR could be related also to the fact that this index takes into account the solar radiation and describes the ability of the plant canopy to intercept the incident radiation available for the photosynthesis (Campillo et al, 2012). The optimal acquisition period identified for f PAR is in agreement with other studies on wheat that showed the spike growth and the grain filling stage as the most sensitive periods to solar radiation.…”
Section: Modis Vegetation Indices In Forecasting Purposessupporting
In the Mediterranean, durum wheat is one of the major crops, but a high variability of grain yield and protein concentration (GPC) prevents an adequate agronomic planning at the farm or consortium level. Although there are many studies on monitoring of crop production and early prediction of yields, little has been done at the local scale. The aim of this study was to assess simplified integration algorithms (SIAs) for integrating remote sensing information with a crop model, to forecast the GPC and grain yield at the field scale. To this end, the CERES‐Wheat model was run to simulate the seasonal average of grain yield (AVE) and GPC in Val d’ Orcia (Tuscany Region, Italy) during the 2009–2010 and 2010–2011 growing seasons. The performances of different vegetation indices from MODIS imagery in harvest forecasting were assessed and compared. The SIA formulation was based on the simulated AVE and GPC, and on their spatialization in relation to the intraannual variability between the fields described by vegetation indices. The simulated AVE traced the observed trend. The fraction of absorbed photosynthetically active radiation (fPAR) was the best index in describing grain yield, and the related SIA showed at validation good performance at the field scale (r2 = 0.74). Conversely, the SIA was unable to predict GPC due to the low performance of CERES‐Wheat in capturing the interannual variability and to the failure of the fPAR in describing the GPC interfields variability at intermediate canopy reflectance values.
“…The highest harvest forecasting power shown by f PAR could be related also to the fact that this index takes into account the solar radiation and describes the ability of the plant canopy to intercept the incident radiation available for the photosynthesis (Campillo et al, 2012). The optimal acquisition period identified for f PAR is in agreement with other studies on wheat that showed the spike growth and the grain filling stage as the most sensitive periods to solar radiation.…”
Section: Modis Vegetation Indices In Forecasting Purposessupporting
In the Mediterranean, durum wheat is one of the major crops, but a high variability of grain yield and protein concentration (GPC) prevents an adequate agronomic planning at the farm or consortium level. Although there are many studies on monitoring of crop production and early prediction of yields, little has been done at the local scale. The aim of this study was to assess simplified integration algorithms (SIAs) for integrating remote sensing information with a crop model, to forecast the GPC and grain yield at the field scale. To this end, the CERES‐Wheat model was run to simulate the seasonal average of grain yield (AVE) and GPC in Val d’ Orcia (Tuscany Region, Italy) during the 2009–2010 and 2010–2011 growing seasons. The performances of different vegetation indices from MODIS imagery in harvest forecasting were assessed and compared. The SIA formulation was based on the simulated AVE and GPC, and on their spatialization in relation to the intraannual variability between the fields described by vegetation indices. The simulated AVE traced the observed trend. The fraction of absorbed photosynthetically active radiation (fPAR) was the best index in describing grain yield, and the related SIA showed at validation good performance at the field scale (r2 = 0.74). Conversely, the SIA was unable to predict GPC due to the low performance of CERES‐Wheat in capturing the interannual variability and to the failure of the fPAR in describing the GPC interfields variability at intermediate canopy reflectance values.
“…The amount of solar radiation intercepted by a canopy is dictated by many factors including; the leaf angle, size, shape and even the thickness, which together with its chlorophyll concentration are key determinants (Campillo et al, 2012). In this study, the age differences in the Eucalyptus tree plantations had no significant effect on the amount of PAR reaching the understory crops but as expected pruning of the canopy base branches significantly increased the amount of PAR reaching the understory.…”
Section: Discussionsupporting
confidence: 49%
“…To modify existing or create new agroforestry systems, interactions of tree crop mixtures should be well researched to provide needed information especially the newly created patterns for light capture (Johar et al, 2017;Whiting, 2011). The productivity of crop canopies has been quantified using concepts of Leaf Area Index (LAI) and the Crop Growth Rate (CGR) as estimators of the crop's ability to capture light energy available for plant growth (Campillo et al, 2012), therefore necessary tools used to assess productivity in this study. Selection of crop species to be used in agroforestry systems is based on cultural, economic as well as environmental factors but their arrangement and management determine the photosynthetic efficiency of the whole plant-community (Nair, 1993).…”
Competition for scarce land resources between food crops and trees has intensified and there is need for a balance to accommodate both, either in rotation programs or in agroforestry systems. Successful intercropping of Eucalyptus trees with crops is hindered by competition for light between trees and crops, soil nutrient dynamics and the allelopathy from Eucalyptus trees. The aim of this study was to establish and assess the performance of farm crops under Eucalyptus grandis tree plantations so as to evaluate the potential of the trees for agroforestry. The crops i.e. common beans, Irish potatoes and black Nightshade (Solanum villosum) were planted along rows of Eucalyptus trees (3 and 6 years) in plot sizes of 4 m by 2 m adopting a factorial arrangement in RCBD with open field as control. The assessment of the performance of the crops was mainly on; germination, Leaf Area Index (LAI) and possible yields. In addition, the amount of photosynthetically active radiation (PAR) reaching the understory crops was measured. From the results, Germination of crops under trees was higher than in the open field. Germination was delayed under trees when compared to those grown in the open field. The age differences in the Eucalyptus tree plantations had no significant effect on the amount of PAR reaching the understory crops. The leaf area index (LAI) of the understory crops was significantly affected by Eucalyptus trees of different ages (p < .001). The crops grown under Eucalyptus trees gave higher yields compared to crops grown in open fields but not fertilized. The effect of Eucalyptus plantation age significantly affected the yields of beans and potatoes (p < .001) but not nightshade; therefore, the vegetable can be grown under Eucalyptus tree shade without reduction in yield. Irish potato and common beans are potential crops for agroforestry with Eucalyptus trees but need further research as their yields were low and had selected disease incidences.
“…Second, accumulated solar radiation (ASOLAR) is closely associated with vegetation growth, and the amount of energy from ASOLAR denotes the potential heat available for plants' organs to develop from one point to another in a life cycle (Peñuelas & Filella, ). ASOLAR has significant implications in sustaining the metabolic processes of vegetation growth, determining the structure and status of plants, and forming the microclimate and macroclimate surrounding them (Bahnová & Rózová, ; Campillo et al, ). According to Monteith and Moss (), under ideal conditions, the amount of ASOLAR intercepted by a crop is linearly correlated with the amount of dry matter accumulated in the crop.…”
Extensive studies have focused on instantaneous and time-lag impacts of climatic factors on vegetation growth; however, the chronical and accumulative indirect impacts of antecedent climatic factors carrying over for a period of time on vegetation growth, defined as cumulative effects, are less investigated. Here we aimed to disentangle the cumulative effects of climatic factors on vegetation growth by using vegetation indexes and accumulated meteorological data. First, we investigated the explanation and fit of climate changes on vegetation variations by applying stepwise multiple linear regression with Akaike information criterion. Then, we obtained the correlation coefficients and lagged time of climatic factors on vegetation growth whereby partial correlation and time-lag effect analyses. Results showed that (i) consideration of cumulative climate effects increased the explanation and fit of climate changes on vegetation dynamics for more than 77% of vegetated surface with an average global explanation of 68.33%, which was approximately 3.35% higher than the scenario when only time-lag effects were considered; (ii) big differences exhibited in the correlation coefficients and lagged times under the scenarios with cumulative climate effects considered or not; and (iii) positive accumulated temperature (accumulated solar radiation) effects with zero (three-month) time lag dominates most mid-high latitude ecosystems, and negative accumulated temperature effects with three-month delay dominates the temperate arid and semiarid regions and tropical dry ecosystems. By comparison, accumulated precipitation had relatively complex cumulative effects on vegetation growth. We concluded that climatic factors had significant cumulative effects on vegetation growth; consideration of the cumulative effects helps us better understand the climate-vegetation interactions. Key Points: • The climatic factors had significant cumulative impacts on vegetation growth, which were varied by climatic factors and spaces • Additional consideration of the cumulative impacts increased the explanation and fit of climatic factors on plant growth • The correlation coefficients and lagged times of climatic factors on plant growth were very different if we included cumulative impacts Supporting Information: • Supporting Information S1
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