Functional data analysis-based yield modeling in year-round crop cultivation
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC11234900 | biostudies-literature | 2024 May
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Crop yield prediction provides information to policymakers in the agricultural production system. This study used leaf reflectance from a spectroradiometer to model grain yield (GY) and aboveground biomass yield (ABY) of maize (Zea mays L.) at Aba Gerima catchment, Ethiopia. A FieldSpec IV (350-2,500 nm wavelengths) spectroradiometer was used to estimate the spectral reflectance of crop leaves during the grain-filling phase. The spectral vegetation indices, such as enhanced vegetation index (EVI), normalized difference VI (NDVI), green NDVI (GNDVI), soil adjusted VI, red NDVI, and simple ratio were deduced from the spectral reflectance. We used regression analyses to identify and predict GY and ABY at the catchment level. The coefficient of determination (R2), the root mean square error (RMSE), and relative importance (RI) were used for evaluating model performance. The findings revealed that the best-fitting curve was obtained between GY and NDVI (R2 = 0.70; RMSE = 0.065; P < 0.0001; RI = 0.19), followed by EVI (R2 = 0.65; RMSE = 0.024; RI = 0.61; P < 0.0001). While the best-fitting curve was obtained between ABY and GNDVI (R2 = 0.71; RI = 0.24; P < 0.0001), followed by NDVI (R2 = 0.77; RI = 0.17; P < 0.0001). The highest GY (7.18 ton/ha) and ABY (18.71 ton/ha) of maize were recorded at a soil bunded plot on a gentle slope. Combined spectral indices were also employed to predict GY with R2 (0.83) and RMSE (0.24) and ABY with R2 (0.78) and RMSE (0.12). Thus, the maize's GY and ABY can be predicted with acceptable accuracy using spectral reflectance indices derived from spectroradiometer in an area like the Aba Gerima catchment. An estimation model of crop yields could help policy-makers in identifying yield-limiting factors and achieve decisive actions to get better crop yields and food security for Ethiopia.
| S-EPMC9202864 | biostudies-literature
Project description:Biofuel production using microalgae is believed to have the advantage of continuous year-round production over crop plants, which have strong seasonality. However, actual year-round production of microalgal lipids using outdoor mass cultivation has rarely been demonstrated. In our previous study, it was demonstrated that the oleaginous diatom, Fistulifera solaris, was culturable in outdoor bioreactors from spring to autumn, whereas biomass and lipid production in winter failed because F. solaris did not grow below 15 °C. Therefore, another candidate strain that is culturable in winter is required. In this study, a cold-tolerant diatom, Mayamaea sp. JPCC CTDA0820, was selected as a promising candidate for biofuel production in winter. Laboratory-scale characterization revealed that this diatom was culturable at temperatures as low as 10 °C. Subsequently, F. solaris (April-October) and Mayamaea sp. JPCC CTDA0820 (November-March) were cultured in outdoor open-pond bioreactors, wherein year-round production of diatom lipids was successfully demonstrated. The maximal values of areal productivities of biomass and lipids reached to 9.79 and 1.80 g/(m² day) for F. solaris, and 8.62 and 0.92 g/(m² day) for Mayamaea sp. JPCC CTDA0820, respectively. With the combined use of these two diatom species, stable year-round production of microalgal lipids became possible.
| S-EPMC5408240 | biostudies-literature
Project description:The Biden Administration raised its Social Cost of Carbon (SCC) estimate about fivefold based in part on global crop yield decline projections estimated on a meta-analysis data base first published in 2014. The data set contains 1722 records but half were missing at least one variable (usually the change in CO2) so only 862 were available for multivariate regression modeling. By re-examining the underlying sources I was able to recover 360 records and increase the sample size to 1222. Reanalysis on the larger data set yields very different results. While the original smaller data set implies yield declines of all crop types even at low levels of warming, on the full data set global average yield changes are zero or positive even out to 5 °C warming.
| S-EPMC11829979 | biostudies-literature
Project description:The aim of this study was to predict crop growth of year-round cut chrysanthemum (Chrysanthemum morifolium Ramat.) based on an empirical model of potential crop growth rate as a function of daily incident photosynthetically active radiation (PAR, MJ m-2 d-1), using generalized estimated parameters of the expolinear growth equation. For development of the model, chrysanthemum crops were grown in four experiments at different plant densities (32, 48, 64 and 80 plants m-2), during different seasons (planting in January, May-June and September) and under different light regimes [natural light, shading to 66 and 43 % of natural light, and supplementary assimilation light (ASS, 40-48 micro mol m-2 s-1)]. The expolinear growth equation as a function of time (EXPOT) or as a function of incident PAR integral (EXPOPAR) effectively described periodically measured total dry mass of shoot (R2 > 0.98). However, growth parameter estimates for the fitted EXPOPAR were more suitable as they were not correlated to each other. Coefficients of EXPOPAR characterized the relative growth rate per incident PAR integral [rm,i (MJ m-2)-1] and light use efficiency (LUE, g MJ-1) at closed canopy. In all four experiments, no interaction effects between treatments on crop growth parameters were found. rm,i and LUE were not different between ASS and natural light treatments, but were increased significantly when light levels were reduced by shading in the summer experiments. There was no consistent effect of plant density on growth parameters. rm,i and LUE showed hyperbolic relationships to average daily incident PAR averaged over 10-d periods after planting (rm,i) or before final harvest (LUE). Based on those relationships, maximum relative growth rate (rm, g g-1 d-1) and maximum crop growth rate (cm, g m-2 d-1) were described successfully by rectangular hyperbolic relationships to daily incident PAR. In model validation, total dry mass of shoot (Wshoot, g m-2) simulated over time was in good agreement with measured ones in three independent experiments, using daily incident PAR and leaf area index as inputs. Based on these results, it is concluded that the expolinear growth equation is a useful tool for quantifying cut chrysanthemum growth parameters and comparing growth parameter values between different treatments, especially when light is the growth-limiting factor. Under controlled environmental conditions the regression model worked satisfactorily, hence the model may be applied as a simple tool for understanding crop growth behaviour under seasonal variation in daily light integral, and for planning cropping systems of year-round cut chrysanthemum. However, further research on leaf area development in cut chrysanthemum is required to advance chrysanthemum crop growth prediction.
| S-EPMC4244859 | biostudies-literature
Project description:This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.
| S-EPMC7810832 | biostudies-literature
Project description:How insects promote crop pollination remains poorly understood in terms of the contribution of functional trait differences between species. We used meta-analyses to test for correlations between community abundance, species richness and functional trait metrics with oilseed rape yield, a globally important crop. While overall abundance is consistently important in predicting yield, functional divergence between species traits also showed a positive correlation. This result supports the complementarity hypothesis that pollination function is maintained by non-overlapping trait distributions. In artificially constructed communities (mesocosms), species richness is positively correlated with yield, although this effect is not seen under field conditions. As traits of the dominant species do not predict yield above that attributed to the effect of abundance alone, we find no evidence in support of the mass ratio hypothesis. Management practices increasing not just pollinator abundance, but also functional divergence, could benefit oilseed rape agriculture.
| S-EPMC6443707 | biostudies-literature
Project description:Accurate estimation of crop yield predictions is of great importance for food security under the impact of climate change. We propose a data-driven crop model that combines the knowledge advantage of process-based modeling and the computational advantage of data-driven modeling. The proposed model tracks the daily biomass accumulation process during the maize growing season and uses daily produced biomass to estimate the final grain yield. Computational studies using crop yield, field location, genotype and corresponding environmental data were conducted in the US Corn Belt region from 1981 to 2020. The results suggest that the proposed model can achieve an accurate prediction performance with a 7.16% relative root-mean-square-error of average yield in 2020 and provide scientifically explainable results. The model also demonstrates its ability to detect and separate interactions between genotypic parameters and environmental variables. Additionally, this study demonstrates the potential value of the proposed model in helping farmers achieve higher yields by optimizing seed selection.
| S-EPMC10121691 | biostudies-literature
Project description:Climate change impacts require us to reexamine crop growth and yield under increasing temperatures and continuing yearly climate variability. Agronomic and agro-meteorological variables were concorded for a large number of plantings of green bean (Phaseolus vulgaris L.) in three growing seasons over several years from semi-tropical Queensland. Using the Queensland government's SILO meteorological database matched to sowing dates and crop phenology, we derived planting specific agro-meteorological variables. Linear and nonlinear statistical models were used to predict duration of vegetative and pod filling periods and fresh yield using agro-meteorological variables including thermal time, radiation and days of high temperature stress. High temperatures over 27.5∘C and 30∘C in the pod fill period were associated with a lower fresh bean yield. Differences between specific bean growing sites were examined using our bespoke open source software to derive agro-meteorological variables. Agronomically informed statistical models using production data were useful in predicting time of harvest. These methods can be applied to other commercial crops when crop phenology dates are collected.
| S-EPMC11893118 | biostudies-literature
Project description:Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.
| S-EPMC5104953 | biostudies-literature
Project description:Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Yp) calculated by a weather-driven crop growth model, called as the "YpCGM method". We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world's changing climate.
| S-EPMC10362053 | biostudies-literature