Crop Production Model Research Articles

Crop Yield Prediction using CNN

Deep learning is a branch of Machine Learning which is completely based on artificial neural networks, as neural networks are going to mimic the human brain so deep learning is also a kind of mimic of the human brain. Farming is the main occupation of India. Crop yield has a direct impact on nation and international economies annually and the yield predicted plays a significant part in the food management and agriculture sector. The task is to build a prediction model for crop production . A prerequisite of intelligent systems has brought artificial neural networks to become a new technology which provides assorted solutions for complex problems in agriculture research. Performance of the agriculture sector mainly hinges on natural forces such as spatio-temporal distribution of rainfall, temperature, climate etc, with the result any deviation of monsoon from the normal pattern brings about numerous fluctuations in area and production. Crop yield has a direct impact on nation and international economies annually and the yield predicted plays a significant part in the food management and agriculture sector. The task is to build a prediction model for crop production. The basic principle of ANN architecture, Data Modeling for Prediction involves four stages namely historical data analysis (Descriptive), Data preprocessing, modeling of Data and Performance Estimation. First classify data based on different attributes. Regression analysis using CNN, it observes the relation between an independent (predictor) and dependent (target) variables. Based on relation training the model will predict crop yield production. application of ANN in predicting crop yield by using various crop performance features as input parameter

Evaluating ecological sustainability of mechanized and traditional systems of damaskrose production using extended exergy analysis

Environmental resource efficiency is a major challenge in the management of agroecosystems and their sustainability. The Extended Exergy Analysis (EEA) is a novel approach that assesses the total amount of resources extracted from the environment to produce agroecosystem products. This study applied the EEA to compare the sustainable development of Damask rose flower production in two mechanized and traditional systems in Nehbandan County, South Khorasan province, using the data of 2021–2022. The EEA calculated the flows of labor, capital, and environmental costs in the systems based on the first and second thermodynamic laws. The results showed that the traditional system had lower cumulative exergy consumption (CExC) and environmental modification cost than the mechanized system, indicating higher ecological sustainability. The extended exergy indices, such as the special capital conversion of energy and material input (kcap), the special capital conversion of product sale (KCAPEEA), extended exergy cost (eec) and the cumulative degree of perfection (CDP), also revealed that the traditional system was more cost-effective, economical, technologically efficient, and closer to the optimal conditions than the mechanized system. To achieve a sustainable agricultural crop production system, new patterns and models for selecting inputs and reducing environmental costs are recommended.

ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China

Abstract. Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Existing public yield datasets have a coarse spatial resolution, spanning from 1 to 43 km. Although these datasets are useful for analyzing large-scale temporal and spatial change in yield, they cannot deal with small-scale spatial heterogeneity, which happens to be the most significant characteristic of the Chinese farmers' economy. Hence, we generated a 30 m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter-wheat-producing provinces in China for the period 2016–2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel 2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated using in situ measurements and census statistics and indicated a stable performance of the HLM based on calibration datasets across China, with a correlation coefficient (r) of 0.81 and a relative root mean square error (rRMSE) of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and statistical data (p<0.01), indicated by an r (rRMSE) of 0.72** (15.34 %) and 0.69** (19.16 %). The ChinaWheatYield30m is a sophisticated dataset with both high spatial resolution and excellent accuracy; such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling and regional climate evaluation. The ChinaWheatYield30m dataset generated from this study can be downloaded from https://doi.org/10.5281/zenodo.7360753 (Zhao et al., 2022b).

CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada

Abstract. The performance of numerical, statistical, and data-driven diagnostic and predictive crop production modeling relies heavily on data quality for input and calibration or validation processes. This study presents a comprehensive database and the analytics used to consolidate it as a homogeneous, consistent, multidimensional genotype, phenotypic, and environmental database for maize phenotype modeling, diagnostics, and prediction. The data used are obtained from the Genomes to Fields (G2F) initiative, which provides multiyear genomic (G), environmental (E), and phenotypic (P) datasets that can be used to train and test crop growth models to understand the genotype by environment (GxE) interaction phenomenon. A particular advantage of the G2F database is its diverse set of maize genotype DNA sequences (G2F-G), phenotypic measurements (G2F-P), station-based environmental time series (mainly climatic data) observations collected during the maize-growing season (G2F-E), and metadata for each field trial (G2F-M) across the United States (US), the province of Ontario in Canada, and the state of Lower Saxony in Germany. The construction of this comprehensive climate and genomic database incorporates the analytics for data quality control (QC) and consistency control (CC) to consolidate the digital representation of geospatially distributed environmental and genomic data required for phenotype predictive analytics and modeling of the GxE interaction. The two-phase QC–CC preprocessing algorithm also includes a module to estimate environmental uncertainties. Generally, this data pipeline collects raw files, checks their formats, corrects data structures, and identifies and cures or imputes missing data. This pipeline uses machine-learning techniques to fill the environmental time series gaps, quantifies the uncertainty introduced by using other data sources for gap imputation in G2F-E, discards the missing values in G2F-P, and removes rare variants in G2F-G. Finally, an integrated and enhanced multidimensional database was generated. The analytics for improving the G2F database and the improved database called Climate for OMICS (CLIM4OMICS) follow findability, accessibility, interoperability, and reusability (FAIR) principles, and all data and codes are available at https://doi.org/10.5281/zenodo.8002909 (Aslam et al., 2023a) and https://doi.org/10.5281/zenodo.8161662 (Aslam et al., 2023b), respectively.

Design of an Agrivoltaic System with Building Integrated Photovoltaics

Building integrated photovoltaics (BIPVs) are becoming popular as building elements such as windows, roofs, and outer walls. Because BIPVs have both a construction material function and an electricity generation function, they are a promising alternative to sustainable buildings. This study aims to propose a novel agrivoltaic system design that produces crops underneath photovoltaic (PV) modules. Regarding the fact that crop growth is significantly influenced by shading from PV modules, roof BIPVs with different shading ratios can lead to increased crop productivity. Thus, BIPV design should be investigated based on the performance estimation and feasibility evaluation of different shading ratios in an agrivoltaic system. To this end, electricity generation and crop production models are devised by polynomial regression (PR) based on field experiment data collected from the agrivoltaic system at the Agricultural Research Service Center in Naju-si, South Korea. The experiment shows that a shading ratio of 30% allows for the maximization of the profitability of electricity and soybean production in an agrivoltaic system equipped with BIPVs. As a result, this research will contribute to implementing an agrivoltaic system with various BIPVs.

An open-source tool for improving on-farm yield forecasting systems

IntroductionThe increased frequency of extreme climate events, many of them of rapid onset, observed in many world regions, demands the development of a crop forecasting system for hazard preparedness based on both intraseasonal and extended climate prediction. This paper presents a Fortran version of the Crop Productivity Model AquaCrop that assesses climate and soil fertility effects on yield gap, which is crucial in crop forecasting systemsMethodsFirstly, the Fortran version model - AQF outputs were compared to the latest version of AquaCrop v 6.1. The computational performance of both versions was then compared using a 100-year hypothetical experiment. Then, field experiments combining fertility and water stress on productivity were used to assess AQF model simulation. Finally, we demonstrated the applicability of this software in a crop operational forecast system.ResultsResults revealed that the Fortran version showed statistically similar results to the original version (r2 > 0.93 and RMSEn < 11%, except in one experiment) and better computational efficiency. Field data indicated that AQF simulations are in close agreement with observation.ConclusionAQF offers a version of the AquaCrop developed for time-consuming applications, such as crop forecast systems and climate change simulations over large areas and explores mitigation and adaptation actions in the face of adverse effects of future climate change.

The spatial suitability of the natural factors affecting the production of agricultural crops in Qayyarah district

Agricultural production is the basic pillar of food security, but the type of agricultural crops and the quantity of their production are greatly and clearly affected by natural factors of all kinds, and man has evolved in his adaptation to natural conditions in order to serve his various activities, including agricultural activity, but his control in these conditions is limited and still The natural conditions (geological structure, topography, climate, soil, and water resources) are specific to the type and location of the various agricultural activities. The uses of agricultural land are subject to natural controls, since the farmer is in direct contact with it as long as he practices agricultural activity in the open. The current research examined the study of the most important natural factors affecting the production of agricultural crops in Qayyarah district represented by the geological structure and surface appearance represented by (slope) and (elevations) in addition to the soil and its productive capacity and linking it with the production of agricultural crops for the aforementioned region for the year 2021 using geographic information systems techniques. And then building a model for spatial fit and agricultural crop production for the purpose of knowing and identifying the best spatial fit places using spatial analysis tools for the data in Arc GIS V10.8.2 to be of sure benefit as it carries new information extracted or extracted from the spatial analysis. And then the process of matching between the agricultural crop maps with the spatial fit map that was produced. The study showed a close relationship between the natural factors in the study area and the amount of crops planted for the previously mentioned year.

Multi-objective optimization model for uncertain crop production under neutrosophic fuzzy environment: A case study

<abstract> <p>In real world uncertainty exist in almost every problem. Decision-makers are often unable to describe the situation accurately or predict the outcome of potential solutions due to uncertainty. To resolve these complicated situations, which include uncertainty, we use expert descriptive knowledge which can be expressed as fuzzy data. Pakistan, a country with a key geographic and strategic position in South Asia, relies heavily on irrigation for its economy, which involves careful consideration of the limits. A variety of factors can affect yield, including the weather and water availability. Crop productivity from reservoirs and other sources is affected by climate change. The project aims to optimize Kharif and Rabbi crop output in canal-irrigated areas. The optimization model is designed to maximize net profit and crop output during cropping seasons. Canal-connected farmed areas are variables in the crop planning model. Seasonal crop area, crop cultivated area, crop water requirement, canal capacity, reservoir evaporation, minimum and maximum storage, and overflow limits affect the two goals. The uncertainties associated with the entire production planning are incorporated by considering suitable membership functions and solved using the Multi-Objective Neutrosophic Fuzzy Linear Programming Model (MONFLP). For the validity and effectiveness of the technique, the model is tested for the wheat and rice production in Pakistan. The study puts forth the advantages of neutrosophic fuzzy algorithm which has been proposed, and the analyses derived can be stated to deal with yield uncertainty in the neutrosophic environments more effectively by considering the parameters which are prone to abrupt changes characterized by unpredictability.</p> </abstract>

Differences in diffuse photosynthetically active radiation effects on cropland light use efficiency calculated via contemporary remote sensing and crop production models

Diffuse photosynthetically active radiation (PARdiff) is instrumental to the light use efficiency (LUE) of vegetation. Accurately assessing the impact of PARdiff on crop LUE can better our understanding of the carbon cycle in cropland ecosystems. LUE estimates from six remote sensing models (including four big-leaf models and two two-leaf models) and two crop production models were compared with measured FLUXNET LUE data from cropland sites under different PARdiff fraction (FDIFFPAR) intervals. Compared with the FLUXNET observations, the Eddy Covariance-Light Use Efficiency (EC-LUEa) model exhibited the best LUE estimation (R2 = 0.250, RMSE = 0.868 gC·MJ−1, and Bias = −0.005 gC·MJ−1) owing to the use of more accurate calculation scheme of environmental stress factors. LUEs calculated from FLUXNET observational data were positively correlated with FDIFFPAR, but only LUEs simulated by the Moderate Resolution Imaging Spectroradiometer Photosynthesis (MOD17) and Two-Leaf Light Use Efficiency (TL-LUE) models increased with increasing FDIFFPAR. This is attributed to the fact that the MOD17 model divides the crop growth types into cereal and broadleaf, while the TL-LUE model considers the change of light interception with increased FDIFFPAR. Furthermore, the maximum LUE (LUEmax) increased with FDIFFPAR at FLUXNET observational sites, but the eight models could not capture the effects of PARdiff on the crop LUEmax. Among the eight models, the LUEmax–FDIFFPAR relationship simulated by the two-leaf models fluctuated because the crops were divided into sunlit and shaded leaves, while the big-leaf and crop production models used a constant LUEmax and showed a constant LUEmax–FDIFFPAR relationship. Additionally, big-leaf models performed better than two-leaf models for gross primary production (GPP) simulation in the cropland ecosystem, which is related to the planting density and vegetation structure. These results demonstrate the importance of considering the impact of FDIFFPAR on LUEmax in LUE modeling.

Remote sensing-based vegetation indices for monitoring rice crop phenology and productivity in cikakak sub-district, sukabumi regency

Paddy is the main food crop consumed by most of the Indonesian daily. It is supported by the fact that rice consumption reached up to 1.55 tons in 2018, and Sukabumi Regency is among the five largest rice producer in Indonesia. The goal of this study is to determine the rice crop phenology and estimate rice productivity in one year of the planting season. The rice crop phenology was analyzed by comparing vegetation indices such as NDVI, ARVI, and MSAVI in different temporal situations. Vegetation indices derived from Sentinel-2 imageries via Google Earth Engine. A rice crop productivity model developed from the statistical relationship between in-situ-based productivity data and vegetation indices applied to estimate productivity per each paddy field at a sub-district level. Also, the estimation will be associated with elevation data. The results of this study are the pattern of the rice crop phenology and the number of harvesting time in one year-planting season. The rice productivity in the Cikakak sub-district estimates at the range between 6.50 to 8.87 tonnes per hectare. Estimation models utilizing NDVI and MSAVI are showing similar results, which averagely at 8.89 and 8.87 tonnes per hectare, respectively. Rice fields with high productivity are mostly located at 250 to 500 meters above sea level.

The Quantitative Features Analysis of the Nonlinear Model of Crop Production by Hybrid Soft Computing Paradigm

In this study, we provide a discretized system of a continuous dynamical model for enhancing crop production in the presence of insecticides and insects. Crops are assumed to grow logistically but are limited by an insect population that entirely depends on agriculture. To protect crops from insects, farmers use insecticides, and their overmuch use is harmful to human health. We assumed that external efforts are proportional to the gap between actual production and carrying capacity to increase the field’s development potential. We use the Levenberg–Marquardt algorithm (LMA) based on artificial neural networks (NNs) to investigate the approximate solutions for different insecticide spraying rates. “NDSolve” tool in Mathematica generated a data collection for supervised LMA. The NN-LMA approximation’s value is achieved by the training, validation, and testing reference data sets. Regression, error histograms, and complexity analysis help to validate the technique’s robustness and accuracy.

Application of Agent-Based Modeling in Agricultural Productivity in Rural Area of Bahir Dar, Ethiopia

Effective weather forecast information helps smallholder farmers improve their adaptation to climate uncertainties and crop productivity. The main objective of this study was to assess the impact of weather forecast adoption on crop productivity. We coupled agent-based and crop productivity models to study the impact of farmers’ management decisions on maize productivity under different rainfall scenarios in Ethiopia. A household survey was conducted with 100 households from 5 villages and was used to validate the crop model. The agent-based model (ABM) analyzed the farmers’ behaviors in crop management under different dry, wet, and normal rainfall conditions. ABM results and crop data from the survey were then used as input data sources for the crop model. Our results show that farming decisions based on weather forecast information improved yield productivity from 17% to 30% under dry and wet seasons, respectively. The impact of adoption rates due to farmers’ intervillage interactions, connections, radio, agriculture extension services, and forecast accuracy brought additional crop yields into the Kebele compared to non-forecast users. Our findings help local policy makers to understand the impact of the forecast information. Results of this study can be used to develop agricultural programs where rainfed agriculture is common.

Yield trends and variabilities explained by climatic change in coastal and non-coastal areas of Bangladesh

Agricultural activities are shaped by climate cycles, and net negative effects of climate change on crop production are evident from scientific researches. This data-driven analytical study aims to assess the fraction of yield trends and variabilities of six major crops (aus, aman, and boro rice, wheat, potato, and jute) attributable to climatic trends and variabilities in coastal and non-coastal areas of Bangladesh. Mixed effects model was used to detect the effects of temperature and precipitation on the crop production and piecewise regression models to estimate the association between climate and yield variabilities. The predicted yields using the detrended temperature and/or precipitation series were subtracted from the predicted yields using their observed series to obtain the yield impact of temperature and/or precipitation trends. During the major disasters (1970–2017), the coast and non-coast, respectively, had lost 12.10 and 9.56% of their crop production. Climatic records (1970–2017) show that the coastal areas had become 0.35 °C warmer and 579 mm wetter, respectively, with 0.15 and 8.57% greater variance than the mainland. Consequently, negative impacts of climatic trends and variabilities on yield trends and variabilities were found to be higher in the coastal region. On average, 2.75 and 2.91% of the crop yields were lost, respectively, due to climatic trends and variabilities, which is equivalent to 2.4 million tons per year (Mt/yr). Excluding the effects of locations and crop types, temperature and precipitation can explain 12% (≡3.06 Mt./yr) of the crop production variance. Findings suggest that coastal crop production is more susceptible to climate change than the inland. Useful insights provided by this research would help policymakers to develop strategies to make future crop production more stable. Researchers and academicians can also benefit from the generated data and methodological approaches to analyse regional and global scale climatic roles in crop production.

Structure of the information management system of cereal crops cultivation

The problems of constructing and implementing the structure of the information management system (IMS) for the cultivation of grain crops based on the optimization of the choice of agricultural technologies in view of the methods of the management theory, system and compartmental approaches are considered. The objects of management are soil, a cultivated crop and ecology of the agricultural landscape area used. It is concluded that the IMS belongs to the class of adaptive control systems with prediction by a multidimensional dynamic stochastic object. A new quality of the management system, which significantly determines its structure, is shown, namely the presence of a control loop for maintaining soil fertility and ecology in crop rotation and a control loop for the agrocenosis of the crop. A hierarchical block diagram of the object management system with forecasting is proposed, which implements functional transformations of the information flow in the IMS. As a criterion for choosing an alternative agricultural technology, environmental and economic efficiency was used, modified in view of the management goals and the composition of the machine and tractor fleet. The analytical description of the processes of the agrobiosystem at the modern level is based on the compartmental approach with the description of phenomena in the form of differential equations. The content of the compartment describes the process of energy and mass transfer in the system: soil – vegetation cover – ground layer of the air based on functional (theoretical) dynamic simulation. Based on the results of the information review, the possibility of implementation of such a management system has not been currently identified. The use of empirical simulation models in the IMS is unacceptable, since a change in a crop or natural-climatic zone will require the development of a new empirical model. The systems for simulating biophysical processes WOFOST, DSSAT, DSSAT Cropping System (CSM), APSIM and AGROTOOL, using methods of functional dynamic simulation within the framework of the compartmental approach, have been analyzed. The developed IMS structure using the AGROTOOL crop productivity model is implemented on condition that new modules of compartments are created.

Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds

To facilitate understanding and decision making in the food-energy-water (FEW) nexus context, we develop an integrated technology-environment-economics model (ITEEM) at a watershed scale. ITEEM is built as an integration of various models, including models for grain processing, drinking water treatment, and wastewater treatment (technology); a watershed model for hydrology, water quality, crop production, and nutrient cycling (environment); an economics model assessing total benefit, including non-market valuation of environmental benefits. Different data techniques are applied to develop suitable surrogates for computer-based models, including a response matrix method, artificial neural networks, and lookup tables. Empirical equations are applied to develop models of economics and drinking water treatment. The input-output relationships between the models are formulated in a unified computational framework. ITEEM, a spatially semi-distributed dynamic simulation model, can be used to quantify the environmental and socioeconomic impacts of various management practices, technologies, and policy interventions on FEW systems in the Corn Belt.

Downscaling model in agriculture in Western Uzbekistan climatic trends and growth potential along field crops physiological tolerance to low and high temperatures

The Global climate change is becoming an increasing challenge for agriculture. Beyond the increased local occurrence of extreme events high temperatures are becoming an increasingly present limiting factor in crop production. The agriculture in the West of Uzbekistan with very limited rainfalls is highly dependent on irrigation schemes using the Amu Darya water flow. With low Winter (freezing nights with minimum air temperatures of less than 0 °C) and high Summer temperatures (hot days and nights with temperatures above 35 °C during daylight, and minimum air temperatures of more than 20 °C during night time – tropical nights) the local continental arid climate temperatures are a main limiting factor faced by the local agriculture. The arid climate, with a crop production dependant on irrigation, allows putting the focus on temperatures influence on field crops, while rainfalls have barely any influence.In temperate countries the focus has mainly been on low temperatures as a main limiting factor. Freeze is indeed influencing the sowing period and putting crops at early development stages at risk.Even though, the West of Uzbekistan is facing low temperatures over the Winter period which is also challenging the local agriculture, high temperatures are becoming an increasing threat over the Summer period.The present study is analysing day and night temperature trends over the period 1987–1990 and 2013–2017. The observed trends are further compared with data from the Intergovernmental Panel on Climate Change (IPCC) model available on the World Bank open portal. Regression lines have been calculated illustrating the trends over the period. The inter-annual temperature variations are important with a relative standard deviation which ranges between 16 and 50%. The trend is considered as not significant when the relative standard deviation exceeds the variation over the overall time-period.The Day degrees are used to provide an insight into the climatic impact on crop growth along plants physiological tolerance. The day degree methodology has been especially adjusted in the present publication in order to take into account the tolerance of the studied crops to high temperatures.While the hot period is progressively expending into the Spring period, Winters are not becoming much milder limiting the benefit for Winter crops. While the hot days and tropical night event will become predominant over the Summer period the yields in cotton and rice are expected to drop drastically over the second half of the XXIst century. The expected reduction of water inflow of the Amu Darya over the century will further strongly put into question the crop production model in the West of Uzbekistan.The present publication aims at describing the ongoing trends, expectable changes in agricultural production and timelines. It is also illustrating how hot temperatures analysis could be integrated in downscaling models in agriculture in other regions of Uzbekistan and of the world.

Physiological and genetic characterization of heat stress effects in a common bean RIL population.

Heat stress is a major abiotic stress factor reducing crop productivity and climate change models predict increasing temperatures in many production regions. Common bean (Phaseolus vulgaris L.) is an important crop for food security in the tropics and heat stress is expected to cause increasing yield losses. To study physiological responses and to characterize the genetics of heat stress tolerance, we evaluated the recombinant inbred line (RIL) population IJR (Indeterminate Jamaica Red) x AFR298 of the Andean gene pool. Heat stress (HS) conditions in the field affected many traits across the reproductive phase. High nighttime temperatures appeared to have larger effects than maximum daytime temperatures. Yield was reduced compared to non-stress conditions by 37% and 26% in 2016 and 2017 seasons, respectively. The image analysis tool HYRBEAN was developed to evaluate pollen viability (PolVia). A significant reduction of PolVia was observed in HS and higher viability was correlated with yield only under stress conditions. In susceptible lines the reproductive phase was extended and defects in the initiation of seed, seed fill and seed formation were identified reducing grain quality. Higher yields under HS were correlated with early flowering, high pollen viability and effective seed filling. Quantitative trait loci (QTL) analysis revealed a QTL for both pod harvest index and PolVia on chromosome Pv05, for which the more heat tolerant parent IJR contributed the positive allele. Also, on chromosome Pv08 a QTL from IJR improved PolVia and the yield component pods per plant. HS affected several traits during the whole reproductive development, from floral induction to grain quality traits, indicating a general heat perception affecting many reproductive processes. Identification of tolerant germplasm, indicator traits for heat tolerance and molecular tools will help to breed heat tolerant varieties to face future climate change effects.

Ozone flux-effect relationship for early and late sown Indian wheat cultivars: Growth, biomass, and yield

• Sensitivity of early and late sown Indian wheat cultivars to O 3 were evaluated. • O 3 flux-effect relationships were investigated for AGB and yield in wheat plants. • Higher accumulation of POD 6 SPEC was found in early sown than late sown cultivar. • Higher longevity, g max and lower critical level made early sown cultivars more sensitive. • The grain yield loss under EO 3 was 30.5 % in early and 23 % in late sown cultivars. Precise ozone (O 3 ) flux models remain helpful to identify the sensitivity of crops and their cultivars under high O 3 levels by assessing biomass and yield losses under natural climate and future O 3 levels. In this study, we have evaluated the effects of O 3 on growth, biomass, and yield of four Indian wheat cultivars by exposure-linked as well as through flux-effect relationship-based analysis using two years of empirical data. Early and late sown cultivars were exposed to ambient O 3 (AO 3 ) and elevated O 3 i.e., ambient+20 ppb (EO 3 ). The POD 6 SPEC (species-specific phytotoxic O 3 dose above a threshold of 6 nmol m −2 PLA s -1 ) on flag leaves for each cultivar and treatment was derived by using DO 3 SE (Deposition of Ozone for Stomatal Exchange) model. Above-ground biomass was reduced more in early sown cultivars (26.1 %) than late sown (21.3 %) because of the larger negative impacts on plant growth under EO 3 treatment. Loss of grain yield under EO 3 exposure was high in early sown cultivars (30.5 %) compared to late sown (23 %) due to high stomatal O 3 uptake during the O 3 exposure period. Based on model outputs, early sown cultivars showed higher accumulated POD 6 SPEC (1.6–3.75 mmol O 3 m −2 ) compared to late sown cultivar (0.65 to 1.97 mmol m −2 ). A negative linear relationship was obtained in above-ground biomass and grain yield concerning POD 6 SPEC. The relationship showed that 0.284 mmol O 3 m −2 accumulated POD 6 SPEC is responsible for a 5% reduction of grain yield in early sown cultivars, while 0.393 mmol O 3 m -2 accumulation is required for a 5% reduction in yield of late sown cultivars. The results also suggest that O 3 flux-effect relationship could be a valuable tool for assessing and predicting the risk of O 3 on wheat cultivars in India and for crop productivity models for predicting climate change impacts.

Harvest date forecast for soybeans from maximum vegetative development using satellite images

ABSTRACT The knowledge of Sowing Dates (SD), Maximum Vegetative Development Dates (MVDD), and Harvest Dates (HD) of crops is important for estimating and forecasting large-scale productivity. Prior information on harvest date of crops is also useful in optimizing grain reception logistics and improving business decision-making in agro-industrial companies. This study aimed to develop a method to predict the HD of soybeans from MVDD, which was obtained from crop maps in the state of Paraná between 2011/2012 and 2013/2014 crop years. TIMESAT software was used to perform a seasonal trend analysis of enhanced vegetation index (EVI) time series from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. After estimating time intervals between MVDD and HD (MH), applied to each mapped pixel, we calculated the 3 year MH average (average of the interval between MVDD and HD for the 3 crop years) for mesoregion and state levels, respectively. The MH by mesoregion was added to MVDD at a regional level, generating HDFORECAST, which is a prediction of the HD. Our results were compared with information from 44 monitored farms in the state in the 2013/2014 crop year. The findings showed that, for soybeans, MH by mesoregion can be used to predict HD with a Mean Error (ME) of 4.84 days, Mean Absolute Error (MAE) of 6.84 days, Root Mean Square Error (RMSE) of 8.12 days, Willmott modified coefficient (d) of 0.9, and Pearson correlation coefficient (r) of 0.88. Our study has produced valuable information for crop productivity models, as well as for logistics of grain transportation and storage in agro-companies, which can anticipate operations knowing when grains will be harvested and delivered to the centres.

Evaluation of AquaCrop model of cucumber under greenhouse cultivation

AbstractWater consumption in agriculture is impossible without considering relations between water, soil and plant. In this regard, there are various models and developed software in order to evaluate relation between soil, water and crop growth stages. These models can be used for irrigation planning if properly optimized and applied. AquaCrop is one of the known crop models, which was developed by the Food and Agriculture Organization of the United Nations. In order to optimize this model for crop production and irrigation management, an experiment was developed in a hydroponic cucumber greenhouse. Various parameters including water consumption volume, crop yield and leaf area index were measured during a season. A fuzzy control system was utilized for controlling temperature, relative humidity, planting bed moisture, light intensity and carbon dioxide values. The main purpose of designing a control system in the greenhouse is to achieve the desired values of temperature and relative humidity. In this model, evapotranspiration, irrigation requirements and crop yield were simulated. The results show that the AquaCrop model can estimate evapotranspiration with the least error in the greenhouse environment, which is controlled by a fuzzy controller. Also the system has estimated the crop yield and biomass of the product with a good degree of precision and it may support crop production in a greenhouse, including crop management and environmental control.