Crop yield prediction using CERES-Rice vs 4.5 model for the climate variability of different agroclimatic zone of south and north-west plane zone of Bihar (India)

CERES-rice models are being validated and tested across the world and vigorously used in agrotechnology transfer. Crop growth models have been considered as potential tools for simulating growth and yield of crops. Hence, DSSAT v 4.5/ CERES-Rice (Decision Support System for Agro-technology Transfer / Crop Estimation through Resource and Environment Synthesis) was applied to validate the Rice productivity from Bihar State in India. Long term historical weather data (1980-2011) and (1985-2011) from South and North West Alluvial plane zones of Bihar was used for yield analysis. Genetic coefficients required for running the CERES-Rice vs 4.5 model were derived and the performance of the model was tested under the climate variability conditions experienced by these two agroclimatic zones. Management combinations simulated were three transplanting dates (1st, 15th & 30th July) for rice cultivar Rmansuri under rainfed conditions.The results indicated that both the early and late sowing dates result in lower yields as compared to optimum sowing date of 15th July. The simulated phenology and yield were found to be in agreement with observed data suggesting that the calibrated model may be operationally used with routinely observed soil, crop management and weather parameters for Rice yield estimation from these two regions of Bihar.

Greenhouse Automation Using Wireless Sensors and IoT Instruments Integrated with Artificial Intelligence

Automation of greenhouse environment using simple timer-based actuators or by means of conventional control algorithms that require feedbacks from offline sensors for switching devices are not efficient solutions in large-scale modern greenhouses. Wireless instruments that are integrated with artificial intelligence (AI) algorithms and knowledge-based decision support systems have attracted growers’ attention due to their implementation flexibility, contribution to energy reduction, and yield predictability. Sustainable production of fruits and vegetables under greenhouse environments with reduced energy inputs entails proper integration of the existing climate control systems with IoT automation in order to incorporate real-time data transfer from multiple sensors into AI algorithms and crop growth models using cloud-based streaming systems. This chapter provides an overview of such an automation workflow in greenhouse environments by means of distributed wireless nodes that are custom-designed based on the powerful dual-core 32-bit microcontroller with LoRa modulation at 868 MHz. Sample results from commercial and research greenhouse experiments with the IoT hardware and software have been provided to show connection stability, robustness, and reliability. The presented setup allows deployment of AI on embedded hardware units such as CPUs and GPUs, or on cloud-based streaming systems that collect precise measurements from multiple sensors in different locations inside greenhouse environments.

Integrating Oscillator-Based Circadian Clocks with Crop Growth Simulations

Circadian rhythms play critical roles in plant physiology, growth, development, and survival, and their inclusion in crop growth models is essential for high fidelity results, especially when considering climate change. Commonly used circadian clock models are often inflexible or result in complex outputs, limiting their use in general simulations. Here we present a new circadian clock model based on mathematical oscillators that easily adapts to different environmental conditions and produces intuitive outputs. We then demonstrate its utility as an input to Glycine max development models. This oscillator clock model has the power to simplify the inclusion of circadian cycles and photoperiodic effects in crop growth models and to unify experimental data from field and controlled environment observations.

Importance of the description of light interception in crop growth models

Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modelling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle (θ) or that θ distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed θ distribution in the canopies from 3-dimensional canopy reconstructions. In all the canopies, θ distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP-Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping (KCell). Results showed that the (KCell) model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.

Integrating Crop Growth Models with Remote Sensing for Predicting Biomass Yield of Sorghum

Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programs to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (1) augment field phenotyping data and derive hard-to-measure phenotypes and (2) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote sensing and ground reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote sensing measurements combined with manual phenotyping in West Lafayette, Indiana in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, Texas between 2001 and 2018. These trials demonstrated that (1) maximum plant height, final dry biomass, and radiation use efficiency (RUE) of photoperiod sensitive and insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (2) photoperiod sensitive sorghum hybrids exhibited greater biomass production in longer growing environments, and (3) the parameterized and validated models perform well in above ground biomass simulations across years and locations. Crop growth models that integrate remote sensing data offer an efficient approach to parameterise larger plant breeding populations.

An Agent-Based Crop Model Framework for Heterogeneous Soils

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.

Impacts of Spatial Zonation Schemes on Yield Potential Estimates at the Regional Scale

Simulations based on site-specific crop growth models have been widely used to obtain regional yield potential estimates for food security assessments at the regional scale. By dividing a region into nonoverlapping basic spatial units using appropriate zonation schemes, the data required to run a crop growth model can be reduced, thereby improving the simulation efficiency. In this study, we explored the impacts of different zonation schemes on estimating the regional yield potential of the Chinese winter wheat area to obtain the most appropriate spatial zonation scheme of weather sites therein. Our simulated results suggest that the upscaled site-specific yield potential is affected by the zonation scheme and by the spatial distribution of sites. As such, the distribution of a small number of sites significantly affected the simulated regional yield potential under different zonation schemes, and the zonation scheme based on sunshine duration clustering zones could effectively guarantee the simulation accuracy at the regional scale. Using the most influential environmental variable of crop growth models for clustering can get the better zonation scheme to upscale the site-specific simulation results. In contrast, a large number of sites had little effect on the regional yield potential simulation results under the different zonation schemes.