Brief history of agricultural systems modeling

Calibration, testing and application of the AquaCrop model for bean crop under irrigation regimes

Crop growth simulation models relate the soil-water-plant-atmosphere components to estimate the development and yield of plants in different scenarios, enabling the identification of efficient irrigation strategies. The aim of this study was to calibrate crop coefficients for a common bean cultivar (IAPAR 57) and assess the AquaCrop model's efficacy in simulating crop growth under different irrigation regimes (T0 - non-irrigated, T1-fully irrigated, and T2-deficit irrigated) and sowing dates (S1-March 21, S2-April 24, and S3-August 23). Successful calibration was achieved for crop seasons with suitable temperatures to crop growth (S1 and S3). However, during periods with suboptimal temperatures (April 24 season), coupled with reduced irrigation supply (T0 and T2), the AquaCrop model did not appropriately account for the combined effects of thermal and water stresses. Despite adjustments to stress coefficients, this led to an overestimation of crop growth and yield. In long-term simulations, the model successfully replicated the variability of crop water availability over cropping seasons, reflecting the impact of precipitation variations. It recommended irrigation strategies for the study region (irrigate at depletion of 120 and 170% of readily available water for sowing on March 21 and August 24, respectively) to achieve high crop yield (> 2,769 kg ha-1) and water productivity (1,050 to 1,445 kg m-3) with minimal application depths (< 150 mm). While acknowledging the need for improvements in thermal stress calculations, the AquaCrop model demonstrates promising utility in studies and applications where water availability significantly influences crop production.

Sustainable transformation of agriculture requires landscape experiments

Transformation of agriculture to realise sustainable site-specific management requires comprehensive scientific support based on field experiments to capture the complex agroecological process, incite new policies and integrate them into farmers' decisions. However, current experimental approaches are limited in addressing the wide spectrum of sustainable agroecosystem and landscape characteristics and in supplying stakeholders with suitable solutions and measures. This review identifies major constraints in current field experimentation, such as a lack of consideration of multiple processes and scales and a limited ability to address interactions between them. It emphasizes the urgent need to establish a new category of landscape experimentation that empowers agricultural research on sustainable agricultural systems, aiming at elucidating interactions among various landscape structures and functions, encompassing both natural and anthropogenic features. It extensively discusses the key characteristics of landscape experiments and major opportunities to include them in the agricultural research agenda. In particular, simultaneously considering multiple factors, and thus processes at different scales and possible synergies or antagonisms among them would boost our understanding of heterogeneous agricultural landscapes. We also highlight that though various studies identified promising approaches with respect to experimental design and data analysis, further developments are still required to build a fully functional and integrated framework for landscape experimentation in agricultural settings.

Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize

We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m-2 year-1 to 7.5 g m-2 year-1, closing the genetic gain gap with respect to the 8.6 g m-2 year-1 observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Δr) above current prediction approaches of ~30% (Δr=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.

A review on food subsystem simulation models for the water-food-energy nexus: development perspective

Agricultural crops are the primary food source because livestock and poultry products also indirectly depend on crops. A significant obstacle to adopting the water, food, and energy (WFE) nexus is the lack of a comprehensive and easy-to-use simulation model for the food subsystem focusing on crops. By reviewing the articles in Scopus and Google Scholar databases, WFE nexus studies can be divided into two categories: simulation-based and conceptual-based studies of WFE nexus. Based on the developmental perspective on food subsystem modeling in the WFE nexus, the conceptual studies were excluded, and the modeling studies were reviewed. Two points of view can be used for WFE nexus modeling: 1. hard-link modeling and 2. soft-link modeling. Comparing these two types of modeling showed that hard-link modeling cannot model the interrelations of the food subsystem, and this shortcoming is of great importance. This study reviewed the crop growth models (CGMs) used in the WFE nexus system from the development perspective. The technical characteristics of the CGMs have been evaluated according to the requirements of the CGMs. Finally, a checklist based on the criteria defined for the nexus system has been provided, which can guide researchers in choosing the appropriate CGMs for the food subsystem with the nexus approach. The analysis revealed that none of the CGMs studied alone were sufficient to develop a simulation model for the food subsystem with the WFE nexus. However, the AquaCrop model met more criteria.

Integrating Community Ecology into Models of Vector-Borne Virus Transmission

Vector-borne plant viruses are a diverse and dynamic threat to agriculture with hundreds of economically damaging viruses and insect vector species. Mathematical models have greatly increased our understanding of how alterations of vector life history and host-vector-pathogen interactions can affect virus transmission. However, insect vectors also interact with species such as predators and competitors in food webs, and these interactions affect vector population size and behaviors in ways that mediate virus transmission. Studies assessing how species' interactions affect vector-borne pathogen transmission are limited in both number and scale, hampering the development of models that appropriately capture community-level effects on virus prevalence. Here, we review vector traits and community factors that affect virus transmission, explore the existing models of vector-borne virus transmission and areas where the principles of community ecology could improve the models and management, and finally evaluate virus transmission in agricultural systems. We conclude that models have expanded our understanding of disease dynamics through simulations of transmission but are limited in their ability to reflect the complexity of ecological interactions in real systems. We also document a need for experiments in agroecosystems, where the high availability of historical and remote-sensing data could serve to validate and improve vector-borne virus transmission models.

Modelling plants across scales of biological organisation for guiding crop improvement

Grain yield improvement in globally important staple crops is critical in the coming decades if production is to keep pace with growing demand; so there is increasing interest in understanding and manipulating plant growth and developmental traits for better crop productivity. However, this is confounded by complex cross-scale feedback regulations and a limited ability to evaluate the consequences of manipulation on crop production. Plant/crop modelling could hold the key to deepening our understanding of dynamic trait-crop-environment interactions and predictive capabilities for supporting genetic manipulation. Using photosynthesis and crop growth as an example, this review summarises past and present experimental and modelling work, bringing about a model-guided crop improvement thrust, encompassing research into: (1) advancing cross-scale plant/crop modelling that connects across biological scales of organisation using a trait dissection-integration modelling principle; (2) improving the reliability of predicted molecular-trait-crop-environment system dynamics with experimental validation; and (3) innovative model application in synergy with cross-scale experimentation to evaluate G×M×E and predict yield outcomes of genetic intervention (or lack of it) for strategising further molecular and breeding efforts. The possible future roles of cross-scale plant/crop modelling in maximising crop improvement are discussed.

Inclusive collaboration across plant physiology and genomics: Now is the time!

Within the broad field of plant sciences, what are the most pressing challenges and opportunities to advance? Answers to this question usually include food and nutritional security, climate change mitigation, adaptation of plants to changing climates, preservation of biodiversity and ecosystem services, production of plant-based proteins and products, and growth of the bioeconomy. Genes and the processes their products carry out create differences in how plants grow, develop, and behave, and thus, the key solutions to these challenges lie squarely in the space where plant genomics and physiology intersect. Advancements in genomics, phenomics, and analysis tools have generated massive datasets, but these data are complex and have not always generated scientific insights at the anticipated pace. Further, new tools may need to be created or adapted, and field-relevant applications tested, to advance scientific discovery derived from such datasets. Meaningful, relevant conclusions and connections from genomics and plant physiological and biochemical data require both subject matter expertise and the collaborative skills needed to work together outside of specific disciplines. Bringing the best expertise to bear on complex problems in plant sciences requires enhanced, inclusive, and sustained collaboration across disciplines. However, despite significant efforts to enable and sustain collaborative research, a variety of challenges persist. Here, we present the outcomes and conclusions of two workshops convened to address the need for collaboration between scientists engaged in plant physiology, genetics, and genomics and to discuss the approaches that will create the necessary environments to support successful collaboration. We conclude with approaches to share and reward collaboration and the need to train inclusive scientists that will have the skills to thrive in interdisciplinary contexts.

Process-Based Crop Modeling for High Applicability with Attention Mechanism and Multitask Decoders

Crop models have been developed for wide research purposes and scales, but they have low compatibility due to the diversity of current modeling studies. Improving model adaptability can lead to model integration. Since deep neural networks have no conventional modeling parameters, diverse input and output combinations are possible depending on model training. Despite these advantages, no process-based crop model has been tested in full deep neural network complexes. The objective of this study was to develop a process-based deep learning model for hydroponic sweet peppers. Attention mechanism and multitask learning were selected to process distinct growth factors from the environment sequence. The algorithms were modified to be suitable for the regression task of growth simulation. Cultivations were conducted twice a year for 2 years in greenhouses. The developed crop model, DeepCrop, recorded the highest modeling efficiency (= 0.76) and the lowest normalized mean squared error (= 0.18) compared to accessible crop models in the evaluation with unseen data. The t-distributed stochastic neighbor embedding distribution and the attention weights supported that DeepCrop could be analyzed in terms of cognitive ability. With the high adaptability of DeepCrop, the developed model can replace the existing crop models as a versatile tool that would reveal entangled agricultural systems with analysis of complicated information.

Spatial modelling of agro-ecologically significant grassland species using the INLA-SPDE approach

The use of spatially referenced data in agricultural systems modelling has grown in recent decades, however, the use of spatial modelling techniques in agricultural science is limited. In this paper, we test an effective and efficient technique for spatially modelling and analysing agricultural data using Bayesian hierarchical spatial models (BHSM). These models utilise analytical approximations and numerical integration called Integrated Nested Laplace Approximations (INLA). We critically analyse and compare the performance of the INLA and INLA-SPDE (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation) approaches against the more commonly used generalised linear model (glm), by modelling binary geostatistical species presence/absence data for several agro-ecologically significant Australian grassland species. The INLA-SPDE approach showed excellent predictive performance (ROCAUC 0.9271-0.9623) for all species. Further, the glm approach not accounting for spatial autocorrelation had inconsistent parameter estimates (switching between significantly positive and negative) when the dataset was subsetted and modelled at different scales. In contrast, the INLA-SPDE approach which accounted for spatial autocorrelation had stable parameter estimates. Using approaches which explicitly account for spatial autocorrelation, such as INLA-SPDE, improves model predictive performance and may provide a significant advantage for researchers by reducing the potential for Type I or false-positive errors in inferences about the significance of predictors.

Sustainability of dairy systems through the lenses of the sustainable development goals

In this paper, we propose to view the sustainability of dairy farming as nested within the sustainability of agriculture, a subset of the sustainability of food systems, which in turn could be construed as a subset of the national commitments of a country to achieve the Sustainable Development Goals (SDGs). Disciplinary, multidisciplinary, and interdisciplinary research are essential to study bio-physical system components and their interactions. However, when dairy farming is viewed as nested within broader societal systems, the inclusion of human elements calls for transdisciplinary research. Few of the 17 SDGs are left untouched by the livestock sector. Research should aim at identifying relevant farm-level metrics that are in alignment with any of the 231 indicators supporting the SDGs. We used two examples to illustrate the approach. In the first, SDG 13 (Climate Action) is used as a reminder that despite the current emphasis on reducing milk carbon footprint (kg CO2-e/kg milk), the contribution of the sector to Climate Action depends on reducing its annual emission (kg CO2-e/year; indicator 13.2.2). In the second example, indicator 2.4.1 (land use for sustainable agriculture) of SDG 2 (Zero Hunger) is used to illustrate the potential tradeoffs between Milk N/Intake N as a metric of nitrogen use efficiency at the cow level and metrics such as the input:output ratio of human-edible protein (Milk N/Intake of human-edible N) that prioritize the use of human-inedible feed in dairy rations as a way to enhance efficiency and circularity at the food system level.

Scientific modelling can be accessible, interoperable and user friendly: A case study for pasture and livestock modelling in Spain

This article describes the adaptation of a non-spatial model of pastureland dynamics, including vegetation life cycle, livestock management and nitrogen cycle, for use in a spatially explicit and modular modelling platform (k.LAB) dedicated to make data and models more interoperable. The aim is to showcase to the social-ecological modelling community the delivery of an existing, monolithic model, into a more modular, transparent and accessible approach to potential end users, regional managers, farmers and other stakeholders. This also allows better usability and adaptability of the model beyond its originally intended geographical scope (the Cantabrian Region in the North of Spain). The original code base (written in R in 1,491 lines of code divided into 13 files) combines several algorithms drawn from the literature in an opaque fashion due to lack of modularity, non-semantic variable naming and implicit assumptions. The spatiotemporal rewrite is structured around a set of 10 namespaces called PaL (Pasture and Livestock), which includes 198 interoperable and independent models. The end user chooses the spatial and temporal context of the analysis through an intuitive web-based user interface called k.Explorer. Each model can be called individually or in conjunction with the others, by querying any PaL-related concepts in a search bar. A scientific dataflow and a provenance diagram are produced in conjunction with the model results for full transparency. We argue that this work demonstrates key steps needed to create more Findable, Accessible, Interoperable and Reusable (FAIR) models beyond the selected example. This is particularly essential in environments as complex as agricultural systems, where multidisciplinary knowledge needs to be integrated across diverse spatial and temporal scales in order to understand complex and changing problems.

Habitat suitability modeling of Descurainia sophia medicinal plant using three bivariate models

Climate change has caused medicinal plants to become increasingly endangered. Descurainia sophia (flixweed) is at risk of extinction in Fars Province, Iran, due to climate change and modifications of land use. Flixweed is highly valuable because of its medicinal properties. The conservation of this species using habitat suitability modeling seems necessary. In this research, the geographical locations of D. sophia's distribution in southern Iran were recorded and mapped using ArcGIS 10.2.2. Then, ten important variables affecting the growth of D. sophia medicinal plants were identified and prepared as thematic layers. These variables were, namely, "elevation," "slope degree," "slope aspect," "soil physical characteristics (sand, silt, and clay percentage)," "soil chemical properties (EC and pH)," "annual mean rainfall," "annual mean temperature," "distance to roads," "distance to rivers," and "plan curvature." In this study, three bivariate models, including the "index-of-entropy (IofE)," "frequency ratio (FR)," and "weight of evidence (WofE)," were used for mapping the habitat suitability of D. sophia. Moreover, the ROC curve and AUC index were used for evaluating the accuracy of the models. Based on the results, the IofE model ("AUC": 0.93) was the most accurate, while the FR ("AUC": 0.92) and WofE ("AUC": 0.90) models ranked second and third, respectively. The models in this study can be applied as tools for the protection of endangered medicinal plants. Furthermore, the map could assist planners, decision-makers, and engineers in extending study areas. By determining the habitat maps of medicinal plants, their extinction can be prevented. Such maps can also assist in the propagation of medicinal plants.

Small-scale farming in drylands: New models for resilient practices of millet and sorghum cultivation

Finger millet, pearl millet and sorghum are amongst the most important drought-tolerant crops worldwide. They constitute primary staple crops in drylands, where their production is known to date back over 5000 years ago. Compared to other crops, millets and sorghum have received less attention until very recently, and their production has been progressively reduced in the last 50 years. Here, we present new models that focus on the ecological factors driving finger millet, pearl millet and sorghum traditional cultivation, with a global perspective. The interaction between environment and traditional agrosystems was investigated by Redundancy Analysis of published literature and tested against novel ethnographic data. Contrary to earlier beliefs, our models show that the total annual precipitation is not the most determinant factor in shaping millet and sorghum agriculture. Instead, our results point to the importance of other variables such as the duration of the plant growing cycle, soil water-holding capacity or soil nutrient availability. This highlights the potential of finger millet, pearl millet and sorghum traditional cultivation practices as a response to recent increase of aridity levels worldwide. Ultimately, these practices can play a pivotal role for resilience and sustainability of dryland agriculture.

Agroecosystem engineering extended from plant-microbe interactions revealed by multi-omics data

In an agroecosystem, plants and microbes coexist and interact with environmental factors such as climate, soil, and pests. However, agricultural practices that depend on chemical fertilizers, pesticides, and frequent tillage often disrupt the beneficial interactions in the agroecosystem. To reconcile the improvement of crop performance and reduction in environmental impacts in agriculture, we need to understand the functions of the complex interactions and develop an agricultural system that can maximize the potential benefits of the agroecosystem. Therefore, we are developing a system called the agroecosystem engineering system, which aims to optimize the interactions between crops, microbes, and environmental factors, using multi-omics analysis. This review first summarizes the progress and examples of omics approaches, including multi-omics analysis, to reveal complex interactions in the agroecosystem. The latter half of this review discusses the prospects of data analysis approaches in the agroecosystem engineering system, including causal network analysis and predictive modeling.

The applicability evaluation and drought validation of the WOFOST model for the simulation of winter wheat growth in Shandong Province, China

The yield of winter wheat in Shandong Province is of great significance for ensuring regional and national food security. To reduce the risk of production loss, the WOFOST model was used to simulate the winter wheat growth to obtain the quantitative and dynamic information. Based on the observational data, a moisture control experiment and the trial and error method, the applicability and drought simulation of the WOFOST model were evaluated for winter wheat growth. For the simulation of the seedling period, flowering period, and maturity period of winter wheat in Shandong Province, the R² were 0.95, 0.69, and 0.68 respectively. The D-index were 0.99, 0.89, and 0.86 respectively. The mean absolute error (mAE) were 1.3, 4.3, and 4.1 respectively. And the nRMSE were 0.65%, 4.3%, and 3.2%, respectively. For the yield simulation, the R², D-index, mean relative error (mRE), and nRMSE were 0.48, 0.82, 10.5% and 12.8%, respectively. For the yield simulation under drought stress, the R², D-index, mRE, and nRMSE were 0.77, 0.93, 7.1%, and 7.4%, respectively. An evaluation index system was built through four different degrees of drought treatment between the jointing period and the flowering period. With the aggravation of drought, the growth indicators about the total above ground production (TAGP), maximum leaf area index (MAXLAI), total dry weight of leaves (TWLV), and total dry weight of stems (TWST) decreasing by 13.6-41.0%, 37.8-56.5%, 19.4-42.1%, and 20.3-51.2%, respectively. The results showed that this model could adequately simulate the formation process of yield under both normal and drought conditions.

SIMULATION MODELING OF A HIGH-THROUGHPUT OYSTER CRYOPRESERVATION PATHWAY

The genetic resources of oysters in Louisiana and the Gulf of Mexico are threatened due to high unexplained oyster mortality. Germplasm repositories are collections of cryopreserved genetic material stored alongside associated information that are used to protect genetics resources and facilitate breeding programs in agricultural industries. Therefore, there is great need for oyster repositories. Development of repositories has been slow despite research on high-throughput cryopreservation protocols because of logistical complexities. The goal of this study was to begin to address the gap between cryopreservation research and repository development in oyster aquaculture by modeling a cryopreservation protocol to understand and improve the process. The steps of a high-throughput cryopreservation protocol were defined and mapped in a process flow diagram. A simulation model was created using time study data, and key bottlenecks in the process were identified. Finally, model variations using alternate types of devices (tools or equipment) were created to address the identified bottlenecks. The model was found to accurately simulate the cryopreservation process. Parameters such as number of straws frozen per oyster, batch size, and number of operators significantly affected how the model performed and device choices produced substantial improvements. Simulation modeling has the potential to inform how cryopreservation pathways and repository systems in aquatic species should be structured and operated. There is ample opportunity for future work such as analyzing the impacts of production scale on cryopreservation processes.

Uncovering the Research Gaps to Alleviate the Negative Impacts of Climate Change on Food Security: A Review

Climatic variability has been acquiring an extensive consideration due to its widespread ability to impact food production and livelihoods. Climate change has the potential to intersperse global approaches in alleviating hunger and undernutrition. It is hypothesized that climate shifts bring substantial negative impacts on food production systems, thereby intimidating food security. Vast developments have been made addressing the global climate change, undernourishment, and hunger for the last few decades, partly due to the increase in food productivity through augmented agricultural managements. However, the growing population has increased the demand for food, putting pressure on food systems. Moreover, the potential climate change impacts are still unclear more obviously at the regional scales. Climate change is expected to boost food insecurity challenges in areas already vulnerable to climate change. Human-induced climate change is expected to impact food quality, quantity, and potentiality to dispense it equitably. Global capabilities to ascertain the food security and nutritional reasonableness facing expeditious shifts in biophysical conditions are likely to be the main factors determining the level of global disease incidence. It can be apprehended that all food security components (mainly food access and utilization) likely be under indirect effect via pledged impacts on ménage, incomes, and damages to health. The corroboration supports the dire need for huge focused investments in mitigation and adaptation measures to have sustainable, climate-smart, eco-friendly, and climate stress resilient food production systems. In this paper, we discussed the foremost pathways of how climate change impacts our food production systems as well as the social, and economic factors that in the mastery of unbiased food distribution. Likewise, we analyze the research gaps and biases about climate change and food security. Climate change is often responsible for food insecurity issues, not focusing on the fact that food production systems have magnified the climate change process. Provided the critical threats to food security, the focus needs to be shifted to an implementation oriented-agenda to potentially cope with current challenges. Therefore, this review seeks to have a more unprejudiced view and thus interpret the fusion association between climate change and food security by imperatively scrutinizing all factors.

Performance and Obstacle Tracking to Qilian Mountains’ Ecological Resettlement Project: A Case Study on the Theory of Public Value

In Gansu Province, China, Wuwei City is an ecologically fragile migration area at the intersection of the Loess Plateau, the Qinghai-Tibet Plateau, and the Mongolia-New Plateau. Using the Triangular Fuzzy TOPSIS method and the obstacle factor diagnostic model, the implementation performance and follow-up support issues of the Ecological Resettlement Project were analyzed from the perspective of the eco-migrant and the public value theory. In contrast to traditional performance appraisal methods, Triangular Fuzzy TOPSIS breaks through the ambiguity of complex environments and subjective information and effectively quantifies the fuzziness of evaluator language variables to improve the validity. The overall performance of the Ecological Resettlement Project in Wuwei is good; the average closeness degree of process is higher than outcome. Migrants rated the fairness of the project as the highest, followed by support, economic outcome, and satisfaction. Gulang County’s performance in the Ecological Resettlement Project is the best, followed by Tianzhu County and Liangzhou District. Project obstacles were mostly related to the outcome dimension, especially the ecological restoration and management, the return to poverty risk, and industrial development on the resettlement site. The main obstacle to the process dimension is migrant satisfaction with government subsidies. Research results provide case study support and experience inspiration for migrant relocation models and their long-term livelihood improvement in the context of rural revitalization.

Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data

BACKGROUND

Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework.

RESULTS

We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model.

CONCLUSIONS

The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

Remote Sensing, Geophysics, and Modeling to Support Precision Agriculture—Part 2: Irrigation Management

Food and water security are considered the most critical issues globally due to the projected population growth placing pressure on agricultural systems. Because agricultural activity is known to be the largest consumer of freshwater, the unsustainable irrigation water use required by crops to grow might lead to rapid freshwater depletion. Precision agriculture has emerged as a feasible concept to maintain farm productivity while facing future problems such as climate change, freshwater depletion, and environmental degradation. Agriculture is regarded as a complex system due to the variability of soil, crops, topography, and climate, and its interconnection with water availability and scarcity. Therefore, understanding these variables’ spatial and temporal behavior is essential in order to support precision agriculture by implementing optimum irrigation water use. Nowadays, numerous cost- and time-effective methods have been highlighted and implemented in order to optimize on-farm productivity without threatening the quantity and quality of the environmental resources. Remote sensing can provide lateral distribution information for areas of interest from the regional scale to the farm scale, while geophysics can investigate non-invasively the sub-surface soil (vertically and laterally), mapping large spatial and temporal domains. Likewise, agro-hydrological modelling can overcome the insufficient on-farm physicochemical dataset which is spatially and temporally required for precision agriculture in the context of irrigation water scheduling.

Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China

Developing an accurate crop yield predicting system at a large scale is of paramount importance for agricultural resource management and global food security. Earth observation provides a unique source of information to monitor crops from a diversity of spectral ranges. However, the integrated use of these data and their values in crop yield prediction is still understudied. Here we proposed the combination of environmental data (climate, soil, geography, and topography) with multiple satellite data (optical-based vegetation indices, solar-induced fluorescence (SIF), land surface temperature (LST), and microwave vegetation optical depth (VOD)) into the framework to estimate crop yield for maize, rice, and soybean in northeast China, and their unique value and relative influence on yield prediction was assessed. Two linear regression methods, three machine learning (ML) methods, and one ML ensemble model were adopted to build yield prediction models. Results showed that the individual ML methods outperformed the linear regression methods, the ML ensemble model further improved the single ML models. Moreover, models with more inputs achieved better performance, the combination of satellite data with environmental data, which explained 72%, 69%, and 57% of maize, rice, and soybean yield variability, respectively, demonstrated higher yield prediction performance than individual inputs. While satellite data contributed to crop yield prediction mainly at the early-peak of the growing season, climate data offered extra information mainly at the peak-late season. We also found that the combined use of EVI, LST and SIF has improved the model accuracy compared to the benchmark EVI model. However, the optical-based vegetation indices shared similar information and did not provide much extra information beyond EVI. The within-season yield forecasting showed that crop yields can be satisfactorily forecasted at two to three months prior to harvest. Geography, topography, VOD, EVI, soil hydraulic and nutrient parameters are more important for crop yield prediction.

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.

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

The smart use of water is a key factor in increasing food production. Over the years, irrigation has relied on historical data and traditional management policies. Control techniques have been exploited to build automatic irrigation systems based on climatic records and weather forecasts. However, climate change and new sources of information motivate better irrigation strategies that might take advantage of the new sources of information in the spectrum of systems and control methodologies in a more systematic way. In this connection, two open questions deserve interest: (i) How can one deal with the space–time variability of soil conditions? (ii) How can one provide robustness to an irrigation system under unexpected environmental change? In this review, the different elements of an automatic control system are described, including the mathematical modeling of the crop–soil systems, instrumentation and actuation, model identification and validation from experimental data, estimation of non-measured variables and sensor fusion, and predictive control based on crop–soil and weather models. An overview of the literature is given, and several specific examples are worked out for illustration purposes.

Climate-associated major food crops production change under multi-scenario in China

To inform targeted adaptation measures, comprehensive assessments of climate change impacts on agricultural systems are urgently needed. The current study analyzed the production (including phenology, yield, ET, and WUE) of major crops in the near future (2011-2040) through probabilistic assessment. The Crop-Environment Resource Synthesis (CERES)-Wheat/Maize model was driven by ensemble climate projections from five global climate models (GCMs) under three emission scenarios (RCP2.6, RCP4.5, RCP8.5). Results showed that: (1) Compared with the base period, the probability of advanced maturity for wheat and maize was 90.36-91.18% and 62.96-64.50%, respectively. The probability of yield reduction for wheat and maize was 64.12-68.93% and 40.44-41.41%, respectively. The probability of water use efficiency (WUE) reduction for wheat and maize was 51.09-53.94% and 35.86-37.93%, respectively. (2) In the absence of adaptation measures, substantial yield loss was found in major crop-producing areas, including the northern winter wheat planting area and Huang-Huai Plain spring-summer maize zone. The spatial overlap of the vulnerable area will exacerbate food insecurity. (3) The decrease in wheat yield and WUE were both greater than that of maize. Replacing highly sensitive crops with heat-tolerant varieties and dietary diversity should be advocated to cope with future climate change. The results will contribute to adaptive decision-making in China.

Fruit tree crop models: an update

Functional structural plant models of tree crops are useful tools that were introduced more than two decades ago. They can represent the growth and development of a plant through the in silico simulation of the 3D architecture in connection with physiological processes. In tree crops, physiological processes such as photosynthesis, carbon allocation and growth are usually integrated into these models, although other functions such as water and nutrient uptake are often disregarded. The implementation of the 3D architecture involves different techniques such as L-system frameworks, pipe model concepts and Markovian models to simulate branching processes, bud fates and elongation of stems based on the production of metamers. The simulation of root architecture is still a challenge for researchers due to a limited amount of information and experimental issues in dealing with roots, because root development is not based on the production of metamers. This review aims to focus on functional-structural models of fruit tree crops, highlighting their physiological components. The potential and limits of these tools are reviewed to point out the topics that still need more attention.

An Analysis of Agricultural Systems Modelling Approaches and Examples to Support Future Policy Development under Disruptive Changes in New Zealand

Agricultural systems have entered a period of significant disruption due to impacts from change drivers, increasingly stringent environmental regulations and the need to reduce unwanted discharges, and emerging technologies and biotechnologies. Governments and industries are developing strategies to respond to the risks and opportunities associated with these disruptors. Modelling is a useful tool for system conceptualisation, understanding, and scenario testing. Today, New Zealand and other nations need integrated modelling tools at the national scale to help industries and stakeholders plan for future disruptive changes. In this paper, following a scoping review process, we analyse modelling approaches and available agricultural systems’ model examples per thematic applications at the regional to national scale to define the best options for the national policy development. Each modelling approach has specificities, such as stakeholder engagement capacity, complex systems reproduction, predictive or prospective scenario testing, and users should consider coupling approaches for greater added value. The efficiency of spatial decision support tools working with a system dynamics approach can help holistically in stakeholders’ participation and understanding, and for improving land planning and policy. This model combination appears to be the most appropriate for the New Zealand national context.

Agricultural land systems importance for supporting food security and sustainable development goals: A systematic review

Agriculture provides the largest share of food supplies and ensures a critical number of ecosystem services (e.g., food provisioning). Therefore, agriculture is vital for food security and supports the Sustainable Development Goal (SDGs) 2 (SDG 2 - zero hunger) as others SDG's. Several studies have been published in different world areas with different research directions focused on increasing food and nutritional security from an agricultural land system perspective. The heterogeneity of the agricultural research studies calls for an interdisciplinary and comprehensive systematization of the different research directions and the plethora of approaches, scales of analysis, and reference data used. Thus, this work aims to systematically review the contributions of the different agricultural research studies by systematizing the main research fields and present a synthesis of the diversity and scope of research and knowledge. From an initial search of 1151 articles, 260 meet the criteria to be used in the review. Our analysis revealed that most articles were published between 2015 and 2019 (59%), and most of the case studies were carried out in Asia (36%) and Africa (20%). The number of studies carried out in the other continents was lower. In the last 30 years, most of the research was centred in six main research fields: land-use changes (28%), agricultural efficiency (27%), climate change (16%), farmer's motivation (12%), urban and peri-urban agriculture (11%), and land suitability (7%). Overall, the research fields identified are directly or indirectly linked to 11 of the 17 SDGs. There are essential differences in the number of articles among research fields, and future efforts are needed in the ones that are less represented to support food security and the SDGs.

A protocol to build soil descriptions for APSIM simulations

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Introducing the models and user interface for characterising a soil in APSIM simulations.

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Listing and describing the parameters needed for building soil descriptions in APSIM.

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Providing recommendations for good practice when setting up soil parameters in APSIM.

Soil processes have a major impact on agroecosystems, controlling water and nutrient cycling, regulating plant growth and losses to the wider environment. Process-based agroecosystem simulation models generally encompass detailed descriptions of the soil, including a wide number of parameters that can be daunting to users with a limited soil science background. In this work we review and present an abridged description of the models used to simulate soil processes in the APSIM (Agricultural Production Systems sIMulator) framework. Such a resource is needed because this information is currently spread over multiple publications and some elements have become outdated. We list and briefly describe the parameters, and establish a protocol with guidelines, for building a soil description for APSIM. This protocol will promote consistency, enhancing the quality of the science done employing APSIM, and provide an easier pathway for new users. This compilation should also be of relevance to users of other models that require detailed soil information.•

This paper presents a brief description of the models for simulating soil processes in the APSIM model.

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The method stablishes guidelines to define the parameters for building a soil description for APSIM.

Integration of Crop Growth Models and Genomic Prediction

Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.

Suitability of operational N direct field emissions models to represent contrasting agricultural situations in agricultural LCA: Review and prospectus

N biogeochemical flows and associated N losses exceed currently planetary boundaries and represent a major threat for sustainability. Measuring N losses is a resource-intensive endeavour, and not suitable for ex-ante assessments, thus modelling is a common approach for estimating N losses associated with agricultural scenarios (systems, practices, situations). The aim of this study is to review some of the N models commonly used for estimating direct field emissions of agricultural systems, and to assess their suitability to systems featuring contrasted agricultural and pedoclimatic conditions. Simple N models were chosen based on their frequent use in LCA, including ecoinvent v3, Indigo-N v1/v2, AGRIBALYSE v1.2/v1.3, and the Mineral fertiliser equivalents (MFE) calculator. Model sets were contrasted, among them and with the dynamic crop model STICS, regarding their consideration of the biophysical processes determining N losses to the environment from agriculture, namely plant uptake, nitrification, denitrification, NH₃ volatilisation, NO₃ leaching, erosion and run-off, and N₂O emission to air; using four reference agricultural datasets. Models' consideration of management drivers such as crop rotations and the allocation of fertilisers and emissions among crops in a crop rotation, over-fertilisation and fertilisation technique, were also contrasted, as well as their management of the mineralisation of soil organic matter and organic fertilisers, and of drainage regimes. For the four agricultural datasets, the ecoinvent model predicted significantly lower values for NH₃ than AGRIBALYSE and STICS. For N₂O, no significant differences were found among models. For NO₃, ecoinvent and AGRIBALYSE predicted significantly higher emissions than STICS, regardless of the fertilisation regime. For both emissions, values of Indigo-N were close to those of STICS. By analysing the reasons for such differences, and the underlying factors considered by models, a list of recommendations was produced regarding more accurate ways to model N losses (e.g. by including the main drivers regulating emissions).

Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet

Digitalization provides access to an integrated network of unexploited big data with potential benefits for society and the environment. The development of smart systems connected to the internet of things can generate unique opportunities to strategically address challenges associated with the United Nations Sustainable Development Goals (SDGs) to ensure an equitable, environmentally sustainable, and healthy society. This perspective describes the opportunities that digitalization can provide towards building the sustainable society of the future. Smart technologies are envisioned as game-changing tools, whereby their integration will benefit the three essential elements of the food-water-energy nexus: (i) sustainable food production; (ii) access to clean and safe potable water; and (iii) green energy generation and usage. It then discusses the benefits of digitalization to catalyze the transition towards sustainable manufacturing practices and enhance citizens' health wellbeing by providing digital access to care, particularly for the underserved communities. Finally, the perspective englobes digitalization benefits by providing a holistic view on how it can contribute to address the serious challenges of endangered planet biodiversity and climate change.

Leaf Apoplast of Field-Grown Potato Analyzed by Quantitative Proteomics and Activity-Based Protein Profiling

Multiple biotic and abiotic stresses challenge plants growing in agricultural fields. Most molecular studies have aimed to understand plant responses to challenges under controlled conditions. However, studies on field-grown plants are scarce, limiting application of the findings in agricultural conditions. In this study, we investigated the composition of apoplastic proteomes of potato cultivar Bintje grown under field conditions, i.e., two field sites in June-August across two years and fungicide treated and untreated, using quantitative proteomics, as well as its activity using activity-based protein profiling (ABPP). Samples were clustered and some proteins showed significant intensity and activity differences, based on their field site and sampling time (June-August), indicating differential regulation of certain proteins in response to environmental or developmental factors. Peroxidases, class II chitinases, pectinesterases, and osmotins were among the proteins more abundant later in the growing season (July-August) as compared to early in the season (June). We did not detect significant differences between fungicide Shirlan treated and untreated field samples in two growing seasons. Using ABPP, we showed differential activity of serine hydrolases and β-glycosidases under greenhouse and field conditions and across a growing season. Furthermore, the activity of serine hydrolases and β-glycosidases, including proteins related to biotic stress tolerance, decreased as the season progressed. The generated proteomics data would facilitate further studies aiming at understanding mechanisms of molecular plant physiology in agricultural fields and help applying effective strategies to mitigate biotic and abiotic stresses.

Mitigation potential of global ammonia emissions and related health impacts in the trade network

Ammonia (NH3) emissions, mainly from agricultural sources, generate substantial health damage due to the adverse effects on air quality. NH3 emission reduction strategies are still far from being effective. In particular, a growing trade network in this era of globalization offers untapped emission mitigation potential that has been overlooked. Here we show that about one-fourth of global agricultural NH3 emissions in 2012 are trade-related. Globally they induce 61 thousand PM2.5-related premature mortalities, with 25 thousand deaths associated with crop cultivation and 36 thousand deaths with livestock production. The trade-related health damage network is regionally integrated and can be characterized by three trading communities. Thus, effective cooperation within trade-dependent communities will achieve considerable NH3 emission reductions allowed by technological advancements and trade structure adjustments. Identification of regional communities from network analysis offers a new perspective on addressing NH3 emissions and is also applicable to agricultural greenhouse gas emissions mitigation.

Missing Links in Predicting Berry Sunburn in Future Vineyards

Sunburn in grapevine berries is known as a recurring disorder causing severe yield losses and a decline in berry quality. The transition from healthy to sunburnt along a temporal trajectory is not fully understood. It is driven by light-boosted local heat impact and modulated by, e.g., past environments of the berry and its developmental state. Events of berry sunburn are often associated with heatwaves, indicating a link to climate change. In addition, the sensitivity of grapevine architecture to changing environmental condition indicates an urgent need to investigate and adapt mitigation strategies of berry sunburn in future vineyards. In this perspective, we want to identify missing links in predicting berry sunburn in vineyards and propose a modeling framework that may help us to investigate berry sunburn in future vineyards. For this, we propose to address open issues in both developing a model of berry sunburn and considering dynamic canopy growth, and canopy interaction with the environment and plant management such as shoot positioning or leaf removal. Because local environmental conditions drive sunburn, we aim at showing that identifying sunburn-reducing strategies in a vineyard under future environmental conditions can be supported by a modeling approach that integrates effects of management practices over time and takes grapevine architecture explicitly into account. We argue that functional-structural plant models may address such complex tasks. Once open issues are solved, they might be a promising tool to advance our knowledge on reducing risks of berry sunburn in silico.

Development of Growth Estimation Algorithms for Hydroponic Bell Peppers Using Recurrent Neural Networks

As smart farms are applied to agricultural fields, the use of big data is becoming important. In order to efficiently manage smart farms, relationships between crop growth and environmental conditions are required to be analyzed. From this perspective, various artificial intelligence algorithms can be used as useful tools to quantify this relationship. The objective of this study was to develop and validate an algorithm that can interpret the crop growth rate response to environmental factors based on a recurrent neural network (RNN), and to evaluate the algorithm accuracy compared to the process-based model (PBM). The algorithms were trained with data from three growth periods. The developed methods were used to measure the crop growth rate. The algorithm consisted of eight environmental variables days after transplanting and two crop growth characteristics as input variables producing weekly crop growth rates as output. The RNN-based crop growth rate estimation algorithm was validated using data collected from a commercial greenhouse. The CropGro-bell pepper model was applied to compare and evaluate the accuracy of the developed algorithm. The training accuracies varied from 0.75 to 0.81 in all growth periods. From the validation result, it was confirmed that the accuracy was reliable in the commercial greenhouse. The accuracy of the developed algorithm was higher than that of the PBM. The developed algorithm can contribute to crop growth estimation with a limited number of data.

A Research Road Map for Responsible Use of Agricultural Nitrogen

Nitrogen (N) is an essential but generally limiting nutrient for biological systems. Development of the Haber-Bosch industrial process for ammonia synthesis helped to relieve N limitation of agricultural production, fueling the Green Revolution and reducing hunger. However, the massive use of industrial N fertilizer has doubled the N moving through the global N cycle with dramatic environmental consequences that threaten planetary health. Thus, there is an urgent need to reduce losses of reactive N from agriculture, while ensuring sufficient N inputs for food security. Here we review current knowledge related to N use efficiency (NUE) in agriculture and identify research opportunities in the areas of agronomy, plant breeding, biological N fixation (BNF), soil N cycling, and modeling to achieve responsible, sustainable use of N in agriculture. Amongst these opportunities, improved agricultural practices that synchronize crop N demand with soil N availability are low-hanging fruit. Crop breeding that targets root and shoot physiological processes will likely increase N uptake and utilization of soil N, while breeding for BNF effectiveness in legumes will enhance overall system NUE. Likewise, engineering of novel N-fixing symbioses in non-legumes could reduce the need for chemical fertilizers in agroecosystems but is a much longer-term goal. The use of simulation modeling to conceptualize the complex, interwoven processes that affect agroecosystem NUE, along with multi-objective optimization, will also accelerate NUE gains.

The Ruminant Farm Systems Animal Module: A Biophysical Description of Animal Management

Dairy production is an important source of nutrients in the global food supply, but environmental impacts are increasingly a concern of consumers, scientists, and policy-makers. Many decisions must be integrated to support sustainable production-which can be achieved using a simulation model. We provide an example of the Ruminant Farm Systems (RuFaS) model to assess changes in the dairy system related to altered animal feed efficiency. RuFaS is a whole-system farm simulation model that simulates the individual animal life cycle, production, and environmental impacts. We added a stochastic animal-level parameter to represent individual animal feed efficiency as a result of reduced residual feed intake and compared High (intake = 94% of expected) and Very High (intake = 88% of expected) efficiency levels with a Baseline scenario (intake = 100% of expected). As expected, the simulated total feed intake was reduced by 6 and 12% for the High and Very High efficiency scenarios, and the expected impact of these improved efficiencies on the greenhouse gas emissions from enteric methane and manure storage was a decrease of 4.6 and 9.3%, respectively.

A Comprehensive Approach to the Design of a Renewable Energy Microgrid for Rural Ethiopia: The Technical and Social Perspectives

In view of Ethiopia’s significant renewable energy (RE) potential and the dynamic interactions among the components of the Water–Energy–Food (WEF) Nexus, we attempted to incorporate solar and small-scale hydropower into the optimal design of an environmentally friendly microgrid with the primary goal of ensuring the sustainability of irrigation water pumping, while taking advantage of existing infrastructure in various small administrative units (kebele). Any additional generated energy would be made available to the community for other needs, such as lighting and cooking, to support health and food security and improve the general quality of life. The novelty of the study stems from the utilization of in situ social data, retrieved during fieldwork interviews conducted in the kebele of interest, to ascertain the actual needs and habits of the local people. Based on these combined efforts, we were able to formulate a realistic energy demand plan for climatic conditions typical of Sub-Saharan Africa agricultural communities and analyze four different scenarios of the microgrid’s potential functionality and capital cost, given different tolerance levels of scheduled outages. We demonstrated that the RE-based microgrid would be socially and environmentally beneficial and its capital cost sensitive to the incorporation of individual or communal machines and appliances. Ultimately, the social impact investigation revealed the design would be welcomed by the local community, whose members already implement tailor-made solutions to support their agricultural activities. Finally, we argue that extended educational programs and unambiguous policies should be in place before any implementation to ensure the venture’s sustainability and functionality.

Meta-Analysis on Energy-Use Patterns of Cropping Systems in Iran

We present a meta-analysis of energy-consumption and environmental-emissions patterns in Iranian cropping systems using data collected from articles published between 2008 and 2018 for 21 different crops. The results show that the crops consuming the most energy per hectare are tomato, sugarcane, cucumber and alfalfa, while sunflower consumed the least. The average total energy input for all crops in Iran during the study period was 48,029 MJ ha−1. Our analysis revealed that potato has the highest potential to reduce energy consumption and that electricity and fertilizer inputs have the most potential for energy savings in cropping systems. Not all studies reviewed addressed the factors that create energy consumption patterns and environmental emissions. Therefore, eight indicators were modeled in this meta-analysis, which include Total Energy Input, Energy Productivity, Energy Use Efficiency, Net Energy, Greenhouse Gas Emissions, Technical Efficiency, Pure Technical Efficiency and Scale Efficiency. The effects of region (which was analyzed in terms of climate), year and crop or product type on these eight indicators were modeled using meta-regression and the nonparametric Kruskal–Wallis test. To create a comprehensive picture and roadmap for future research, the process of the agricultural-systems analysis cycle is discussed. This review and meta-analysis can be used as a guide to provide useful information to researchers working on the energy dynamics of agricultural systems, especially in Iran, and in making their choices of crop types and regions in need of study.

Accurate Imputation of Greenhouse Environment Data for Data Integrity Utilizing Two-Dimensional Convolutional Neural Networks

Greenhouses require accurate and reliable data to interpret the microclimate and maximize resource use efficiency. However, greenhouse conditions are harsh for electrical sensors collecting environmental data. Convolutional neural networks (ConvNets) enable complex interpretation by multiplying the input data. The objective of this study was to impute missing tabular data collected from several greenhouses using a ConvNet architecture called U-Net. Various data-loss conditions with errors in individual sensors and in all sensors were assumed. The U-Net with a screen size of 50 exhibited the highest coefficient of determination values and the lowest root-mean-square errors for all environmental factors used in this study. U-Net50 correctly learned the changing patterns of the greenhouse environment from the training dataset. Therefore, the U-Net architecture can be used for the imputation of tabular data in greenhouses if the model is correctly trained. Growers can secure data integrity with imputed data, which could increase crop productivity and quality in greenhouses.

Change in the Level of Agricultural Development in the Context of Public Institutions’ Activities—A Case Study of the NASC Activities in Poland

Agricultural development is determined by various factors, such as environmental, economic, demographic, or social circumstances. In order to present the level of this development as com-prehensively as possible, a multidimensional analysis should be carried out with an appropriate methodology. In this article, a taxonomic approach known as the Hellwig’s method was used to determine the level of agricultural development. The area of research was the territory of Poland, divided into voivodships, which are the main units of the administrative division of the country. The development of agriculture thus determined was correlated with activities pursued by the National Agricultural Support Centre (NASC), an institution responsible for the management of agricultural real estate owned by the State Treasury in Poland. The results showed that the NASC’s activities are related to the level of agricultural development in every voivodship. The investigated model of rural space management was shown to be a rational one, performing well in today’s market conditions. The proposed methodology could adapt to similar situations and can be used in similar research on rural areas.

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

The Potential of Switchgrass and Miscanthus to Enhance Soil Organic Carbon Sequestration—Predicted by DayCent Model

Warm season perennial C4 grasses (WSGs), switchgrass (Panicum virgatum L.) and miscanthus species (Miscanthus spp.), have been reported to positively influence short-term (15–20 years) soil organic carbon (SOC). In this study, the DayCent model was used to predict changes in long-term SOC stocks under WSGs for moderate (Representative Concentration Pathway (RCP) 4.5) and high (RCP 8.5) warming climate change scenarios in southern Ontario, Canada, and to determine how long the enhanced SOC stock will last when WSGs are converted back to annual crop rotation. The model predicted that a consistent corn–corn–soybean–winter wheat (CCSW) rotation prevented SOC from depletion over the 21st century. Under WSGs, the model predicted high rates of SOC sequestration during the first 20–30 years which then tended to stabilize after 50–60 years. However, the rate of SOC sequestration over 90 years for RCP 4.5 was 0.26 and 0.94 Mg C ha−1 yr−1 for switchgrass and miscanthus, respectively. If 40-year stands of WSGs are converted back to CCSW, the model predicted SOC decline to the previous level in 40–50 years. DayCent predicted that under RCP 8.5 scenario in the second half of the 21st century and in the future, there will be a reduction in SOC stocks, especially under miscanthus stands.

Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements

Novel technologies for estimating crop water needs include mainly remote sensing evapotranspiration estimates and decision support systems (DSS) for irrigation scheduling. This work provides several examples of these approaches, that have been adjusted and modified over the years to provide a better representation of the soil–plant–atmosphere continuum and overcome their limitations. Dynamic crop simulation models synthetize in a formal way the relevant knowledge on the causal relationships between agroecosystem components. Among these, plant–water–soil relationships, water stress and its effects on crop growth and development. Crop models can be categorized into (i) water-driven and (ii) radiation-driven, depending on the main variable governing crop growth. Water stress is calculated starting from (i) soil water content or (ii) transpiration deficit. The stress affects relevant features of plant growth and development in a similar way in most models: leaf expansion is the most sensitive process and is usually not considered when planning irrigation, even though prolonged water stress during canopy development can consistently reduce light interception by leaves; stomatal closure reduces transpiration, directly affecting dry matter accumulation and therefore being of paramount importance for irrigation scheduling; senescence rate can also be increased by severe water stress. The mechanistic concepts of crop models can be used to improve existing simpler methods currently integrated in irrigation management DSS, provide continuous simulations of crop and water dynamics over time and set predictions of future plant–water interactions. Crop models can also be used as a platform for integrating information from various sources (e.g., with data assimilation) into process-based simulations.