Digitization of crop nitrogen modelling: A review

Applying the correct dose of nitrogen (N) fertilizer to crops is extremely important. The current predictive models of yield and soil–crop dynamics during the crop growing season currently combine information about soil, climate, crops, and agricultural practices to predict the N needs of plants and optimize its application. Recent advances in remote sensing technology have also contributed to digital modelling of crop N requirements. These sensors provide detailed data, allowing for real-time adjustments in order to increase nutrient application accuracy. Combining these with other tools such as geographic information systems, data analysis, and their integration in modelling with experimental approaches in techniques such as machine learning (ML) and artificial intelligence, it is possible to develop digital twins for complex agricultural systems. Creating digital twins from the physical field can simulate the impact of different events and actions. In this article, we review the state-of-the-art of modelling N needs by crops, starting by exploring N dynamics in the soil−plant system; we demonstrate different classical approaches to modelling these dynamics so as to predict the needs and to define the optimal fertilization doses of this nutrient. Therefore, this article reviews the currently available information from Google Scholar and ScienceDirect, using relevant studies on N dynamics in agricultural systems, different modelling approaches used to simulate crop growth and N dynamics, and the application of digital tools and technologies for modelling proposed crops. The cited articles were selected following the exclusion criteria, resulting in a total of 66 articles. Finally, we present digital tools and technologies that increase the accuracy of model estimates and improve the simulation and presentation of estimated results to the manager in order to facilitate decision-making processes

Biophysical and Economic Implications for Agriculture of +1.5 and +2.0C Global Warming Using AgMIP Coordinated Global and Regional Assessments

This study presents results of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments (CGRA) of +1.5 and +2.0 C global warming above pre-industrial conditions. This first CGRA application provides multi-discipline, multi-scale, and multi-model perspectives to elucidate major challenges for the agricultural sector caused by direct biophysical impacts of climate changes as well as ramifications of associated mitigation strategies. Agriculture in both target climate stabilizations is characterized by differential impacts across regions and farming systems, with tropical maize (Zea mays) experiencing the largest losses while soy (Glycine max) mostly benefits. The result is upward pressure on prices and area expansion for maize and wheat (Triticum), while soy prices and area decline (results for rice, Oryza sativa, are mixed). An example global mitigation strategy encouraging bioenergy expansion is more disruptive to land use and crop prices than the climate change impacts alone, even in the +2.0 C World which has a larger climate signal and lower mitigation requirement than the +1.5 C World. Coordinated assessments reveal that direct biophysical and economic impacts can be substantially larger for regional farming systems than global production changes. Regional farmers can buffer negative effects or take advantage of new opportunities via mitigation incentives and farm management technologies. Primary uncertainties in the CGRA framework include the extent of CO2 benefits for diverse agricultural systems in crop models, as simulations without CO2 benefits show widespread production losses that raise prices and expand agricultural are

Advancing agricultural research using machine learning algorithms

Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and background management combinations that are not applicable nor translatable to all farms. A method that accurately evaluates the effectiveness of infinite cropping system interactions (involving multiple management practices) to increase maize and soybean yield across the US does not exist. Here, we utilize extensive databases and artificial intelligence algorithms and show that complex interactions, which cannot be evaluated in replicated trials, are associated with large crop yield variability and thus, potential for substantial yield increases. Our approach can accelerate agricultural research, identify sustainable practices, and help overcome future food demands

Climate change and global crop yield: impacts, uncertainties and adaptation

As global mean temperature continues to rise steadily, agricultural systems are projected to face unprecedented challenges to cope with climate change. However, understanding of climate change impacts on global crop yield, and of farmers’ adaptive capacity, remains incomplete as previous global assessments: (1) inadequately evaluated the role of extreme weather events; (2) focused on a small subset of the full range of climate change predictions; (3) overlooked uncertainties related to the choice of crop modelling approach and; (4) simplified the representation of farming adaptation strategies. This research aimed to assess climate change impacts on global crop yield that accounts for the knowledge gaps listed above, based on the further development and application of the global crop model PEGASUS. Four main research topics are presented. First, I investigated the roles of extreme heat stress at anthesis on crop yield and uncertainties related to the use of seventy-two climate change scenarios. I showed large disparities in impacts across regions as extreme temperatures adversely affects major areas of crop production and lower income countries, the latter appear likely to face larger reduction in crop yields. Second, I coordinated the first global gridded crop model intercomparison study, comparing simulations of crop yield and water use under climate change. I found modelled global average crop water productivity increases by up to 17±20.3% when including carbon fertilisation effects, but decreases to –28±13.9% when excluding them; and identified fundamental uncertainties and gaps in our understanding of crop response to elevated carbon dioxide. Third, to link climate impacts with adaptation, I introduced the recently developed concept of representative agricultural pathways and examined their potential use in models to explore farming adaptation options within biophysical and socio-economic constraints. Finally, I explored tradeoffs between increasing nitrogen fertiliser use to close the global maize yield gap and the resulting nitrous oxide emissions. I found global maize production increases by 62% based on current harvested area using intensive rates of nitrogen fertiliser. This raises the share of nitrous oxide emissions associated with maize production from 20 to 32% of global cereal related emissions. Finally, these results demonstrated that in some regions increasing nitrogen fertiliser application, without addressing other limiting factors such as soil nutrient imbalance and water scarcity, could raise nitrous oxide emissions without enhancing crop yield

Bio-economic farm modelling to analyse agricultural land productivity in Rwanda

Keywords: Rwanda; farm household typology; sustainable technology adoption; multivariate analysis; land degradation; food security; bioeconomic model; crop simulation models; organic fertiliser; inorganic fertiliser; policy incentives In Rwanda, land degradation contributes to the low and declining agricultural productivity and consequently to food insecurity. As a result of land degradation and increasing population pressure, there is urgent need to simultaneously enhance food security and agro-ecological sustainability. The main objective of this PhD thesis was to make an assessment of technology options and policy incentives that can enhance sustainable farming in Rwanda. A multivariate analysis approach was used to clearly identify five types of farm households and their socio-economic characteristics. The main differences between the five farm types relate to gender, age, education, risk perception, risk attitude, labour availability, land tenure and income. A bio-economic model capable of analysing the impacts of soil erosion, family planning and land consolidation policies on food security in Rwanda was developed, and applied for one typical farm household. Calculations with the bio-economic model showed that a higher availability of good farm land would increase the farm income. Additionally, preserving soils against erosion and reducing risk would allow for using more marginal land which would increase food production for home consumption and for the market. Increasing the opportunities for off-farm employment can also increase farm household income. The simulation of crop yields under sustainable land management showed that predicted crop yields were distinctly higher than the actual yields for the current small-scale farming practices that are common in the region. Using the developed bio-economic model, model results showed that these sustainable agricultural technologies will clearly enhance food production (after a learning period) and income for all farm household types except the household with the largest farm for which cash at the beginning of the season is too restricted to switch to the new technologies. Provision of credit and availability off-farm activities have emerged as the most serious policies likely to affect the adoption of alternative technologies in all the farm households. The bio-economic farm model and its applications developed in this study give more insights into the possibilities of transforming the current farming system towards more sustainable farming. . </p

Exploring and modelling the effects of agricultural land management and climate change on agroecosystem services in the Eastern Cape, South Africa

The aims of this study were to evaluate the impacts of agricultural land management strategies and climate change on irrigated maize production in the Eastern Cape, South Africa. To achieve these aims, the study was guided by two overarching research questions, subsequently broken down into more specific questions. The first research question examined the reasons behind farmers’ current agricultural land management practices, the values they assigned to different agroecosystem services, their perceptions of climate change and the adaptation strategies they used to address challenges associated with agricultural crop production and climate change. To answer these questions, a survey of conventional farmers in the Eastern Cape was carried out. The survey targeted farmers who used fertilisers and irrigation water in their day to day farming. Results showed that farmers recognised the different benefits that agroecosystems provided even though they were not familiar with the term ‘ecosystem services.’ Farmers assigned a high value to food provisioning compared to other agroecosystem services and managed their farms for maximum crop yields or maximum crop quality. Fertiliser and irrigation water management decisions were based on multiple factors such as cost, availability of farming equipment and crop yield or crop quality considerations. Survey results showed that while most farmers were able to state the amount of fertiliser used per growing season, the majority of farmers did not know the amount of water they used per growing season. From the farmers’ survey it was recommended that extension services and agricultural education programmes be strengthened in the region to increase farmers’ knowledge on effective agricultural land management strategies that support sustainable intensification. The second research question investigated the effects of agricultural land management strategies and climate change on crop yields in the Eastern Cape. This investigation was done in three steps. First, a crop model, the Environmental Policy Integrated Climate (EPIC) model was calibrated and validated using limited field data from maize variety trials carried out at the Cradock Research Farm in the Eastern Cape. Calibration and validation results proved satisfactory with model efficiencies (Nash Sutcliffe, NSE) greater than 0.5 for both calibration and validation. It was concluded that limited data from field trials on maize that only included grain yield and agricultural land management dates can be used for the calibration of the EPIC model to simulate maize production under South African conditions. In the second step, the calibrated model was applied to simulate different irrigation and fertiliser management strategies for maize production in the Eastern Cape. Different irrigation and Nitrogen (N) fertiliser levels were compared to find optimal irrigation and N fertiliser management strategies that would increase maize yields while minimising environmental pollution (nitrate leaching). Model outputs were also compared to the average yields obtained in the field trials (baseline) and to maize yields reported by farmers in the farmers’ survey. Results showed that improved management of irrigation water and N fertiliser could improve farmers’ maize yields from approximately 7.2 t ha-1 to approximately 12.2 t ha-1, an increase of approximately 69%. Results also revealed a trade-off between food provision and nitrate leaching. Simulations showed that increasing N fertiliser application under sufficient irrigation water levels would increase maize yields, however, this would be accompanied by an increase in N leaching. Lastly, the EPIC model was then applied to simulate the effects of future climate change on irrigated maize production in the Eastern Cape. For these simulations, the model was driven by statistically downscaled climate data derived from three General Circulation Models (GCMs) for two future climate periods, (2040-2069) and (2070-2099), under two Representative Concentration Pathways (RCPs): RCP 4.5 and RCP 8.5. Future maize yields were compared to the baseline (1980-2010) maize yield average. All three climate models predicted a decline in maize yields, with yields declining by as much as 23.8% in RCP 8.5, 2070-2099. Simulations also predicted increases in average daily maximum and minimum temperatures for both the two future climate periods under both RCPs. Results also indicated a decrease in seasonal irrigation water requirements. Nitrate leaching was projected to significantly increase towards the end of the century, increasing by as much as 373.8% in RCP 8.5 2070-2099. Concerning farmers’ perceptions of climate change, results showed that farmers were aware of climate change and identified temperature and rainfall changes as the most important changes in climate that they had observed. To adapt to climate change, farmers used a variety of adaptation strategies such as crop rotations and intercropping. Apart from challenges posed by climate change, farmers also faced other challenges such as access to markets and access to financial credit lines, challenges that prevented them from effectively adapting to climate change. The study therefore recommended that appropriate and adequate strategies be designed to help farmers in the region offset the projected decrease in maize production and increase crop yields while minimising negative environmental impacts

Machine-Learning and Meta-Analysis Techniques to Quantify and Predict Soil Organic Carbon, N\u3csub\u3e2\u3c/sub\u3eO-N and CO\u3csub\u3e2\u3c/sub\u3e-C Emissions in Cover Crop Systems

People worldwide are challenged by multiple threats including climate change, growing populations, and soil degradation. Addressing these challenges requires understanding of the local environment, farming systems and modern technologies. These technologies include new ways to process information that include artificial intelligence, machine learning and meta-analysis. Models produced using these technologies may be useful for predicting the consequences of implementing conservation practices that reduce GHG emissions as well as for determining the carbon footprint of cropping systems that include environmentally friendly conservation technologies such as growing cover crop. Therefore, our objectives of this study were to: 1) provide an overview of conservation agriculture technology as strategy to minimize soil degradation, climate change challenges, and food insecurity issues in developing countries like Nepal, 2) conduct global meta-analysis to quantify the impact of cover crops as one of conservation agriculture technique, on soil organic carbon (SOC) and crop yield in a corn (Zea mays L.) cropping system and 3) assess different machine learning based algorithms to predict the daily N2O-N and CO2-C emission from a decomposing rye (scientific name of rye) cover crop. For the first objective, historical data analysis indicated that air temperatures in Nepal have been increasing since 1901 at a rate of y 0.016 oC yr-1, whereas precipitation has been decreasing at a rate of -0.137 mm yr-1. Increasing air temperature, when combined with decreasing precipitation, are interacting to reduce crop growth and yield, diminishing Nepal’s food security. We proposed conservation agriculture practices such as planting cover crop as farmer and environment friendly approach to mitigate and adopt the climate change impact and enhance food security. In second objective, I used meta- analysis approach to measure the effect of cover crop on SOC values in corn at a global scale. During the meta-analysis, data from 62 globally published peer reviewed literature showed that cover crops in the corn production system increased SOC by an average of 7.8%. The SOC increased at rates of 0.46 and 0.80 Mg/ha/year at the 0-15 and 0-30 cm soil depths respectively, due to cover crop planting. To meet the third objective, several different machine learning prediction models were tested, which included multiple linear regression (MLR), partial least square regression (PLSR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN), on daily N2ON and CO2-C emission data which were measured from a decomposing cover crop in 2019 and 2020 at Aurora, SD, USA. Each models’ performance was accessed using coefficient of determination (R2) (higher values close to one were deemed ‘best’), root mean square error (RMSE) and mean absolute error (MAE), where lowest values were ‘best’. Out of all models, the RF model accounted for 73% and 85% of the variability explained in N2O-N and CO2-C emissions, respectively. Across the three objectives, we found that new analysis approaches such as machine learning and meta-analysis can be used to determine the carbon footprint and prediction of GHG emission from conservation agriculture practices such as planting cover crops

Quantification and Machine Learning Based N2O-N and CO2-C Emissions Predictions from a Decomposing Rye Cover Crop

Cover crops improve soil health and reduce the risk of soil erosion. However, their impact on the carbon dioxide equivalence (CO2e) is unknown. Therefore, objective of this two-year study was to quantify the effect of cover crop-induced differences in soil moisture, temperature, organic C, and microorganisms on CO2e and to develop machine learning algorithms that predict daily N2O-N and CO2-C emissions. The prediction models tested were multiple linear regression (MLR), partial least square regression (PLSR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN). Models’ performance was accessed using R2 , RMSE and MAE. Rye (secale cereale) was dormant seeded in mid-October and in the following spring it was terminated at corn’s (Zea mays) V4 growth stage. Soil temperature, moisture, and N2O-N and CO2-C emissions were measured near continuously from soil thaw to harvest in 2019 and 2020. Prior to termination, the cover crop decreased N2O-N emissions by 34% (p=0.05) and over the entire season, N2O-N emissions from cover crop and no cover crop treatments were similar (p=0.71). Based on N2O-N and CO2-C emissions over the entire season and the estimated fixed cover crop carbon remaining in the soil, the partial CO2e were -1,061 and 496 kg CO2e ha-1 in the cover crop and no cover crop treatments, respectively. The RF algorithm explained more of the daily N2O-N (73%) and CO2-C (85%) emissions variability during validation than the other models. Across models, the most important variables were temperature and the amount of cover crop-C added to the soil

XX Convegno nazionale dell'Associazione italiana di Agrometeorologia (AIAM). XLVI Convegno nazionale della Società italiana di Agronomia (SIA). Strategie integrate per affrontare le sfide climatiche e agronomiche nella gestione dei sistemi agroalimentari. Integrated strategies for agro-ecosystem management to address climate change challenges.

Atti del convegno nazionale di due delle principali società scientifiche che si occupano di scienze agrarie (Società Italiana di Agronomia e Associazione Italiana di AgroMeteorologia), quest'anno effettuato congiuntamente. Nel convegno si à trattato dei problemi e delle nuove strategie integrate per affrontare le sfide climatiche e agronomiche nella gestione dei sistemi agroalimentari