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Yu., Tararico, and Lukashuk V. "Intellectual decision-making technology in agricultural production." Artificial Intelligence 27, jai2022.27(1) (June 20, 2022): 219–28. http://dx.doi.org/10.15407/jai2022.01.219.
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Objective assessment of agro-resource potential of regions, understanding of the principles of forming the optimal structure of production in relation to soil and climatic conditions and energy potential, analysis of factors influencing the use of certain means of production, allows producers to make close to optimal current and strategic decisions. To do this, all industrial resources must be considered not separately, but in a complex structure of the agricultural production system in order to ensure the most rational use of them in optimal quantities and interaction. To strengthen the food security and energy independence of the state, it is necessary to form a powerful agricultural sphere of Ukraine. This is achieved through the rational use of agricultural resources, including solar energy through the binding of virtually unlimited resources of nitrogen, carbon, oxygen and hydrogen of the Earth's atmosphere in fats, proteins and hydrocarbons, provided mandatory recycling or reuse of minerals, balanced combination of biological and industrial resources and systematic increase of soil fertility. Therefore, it is necessary to make the transition from the traditional style of enterprise management, based on the production experience and intuition of managers and staff, to modern methods of decision-making that allow for operational and long-term planning with high accuracy and predictability. It is known that the main tool of systems analysis is modeling. The fundamental value of the model lies in its ability to change the real process. For most farms, the farm-wide experimentation procedure is either unacceptable or impractical. Such an experiment has too dangerous consequences for them. Therefore, when analyzing the problem, there is a need for a simulator of the researched enterprise, which could be used for testing instead of the real system. Such a simulator is a model that should reflect the most important patterns of transformation of natural, material, financial, informational, energy and labor resources into agricultural products. The result is a system of interconnected standard modules for determining indicators: production volumes, product prices, the amount of costs, the amount of credit required, the assessment of possible profits and the accumulation of own funds. Each of the considered production or economic indicators can be determined separately. The algorithm of the perspective information system presented in the work allows to comprehensively analyze the action and interaction of individual components of agricultural production and to make close to optimal strategic and current decisions at different levels of agro-industrial complex management.
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Carberry, PS, RL McCown, RC Muchow, JP Dimes, ME Probert, PL Poulton, and NP Dalgliesh. "Simulation of a legume ley farming system in northern Australia using the Agricultural Production Systems Simulator." Australian Journal of Experimental Agriculture 36, no. 8 (1996): 1037. http://dx.doi.org/10.1071/ea9961037.
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An innovative ley farming system, involving cereal crops grown in rotation with pasture legumes, has been tentatively adopted by farmers in the semi-arid tropics of northern Australia. Yet, after more than a decade of experimental research, the long-term
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Ojeda, Jonathan J., Jeffrey J. Volenec, Sylvie M. Brouder, Octavio P. Caviglia, and Mónica G. Agnusdei. "Evaluation of Agricultural Production Systems Simulator as yield predictor ofPanicum virgatumandMiscanthusxgiganteusin several US environments." GCB Bioenergy 9, no. 4 (July 29, 2016): 796–816. http://dx.doi.org/10.1111/gcbb.12384.
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Wajid, Aftab, Khalid Hussain, Ayesha Ilyas, Muhammad Habib-ur-Rahman, Qamar Shakil, and Gerrit Hoogenboom. "Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments." Agriculture 11, no. 11 (November 19, 2021): 1166. http://dx.doi.org/10.3390/agriculture11111166.
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Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha−1 with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments.
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Afroz, Mahnaz Dil, Runwei Li, Khaleel Muhammed, Aavudai Anandhi, and Gang Chen. "Best Management Practices for Sustaining Agricultural Production at Choctawhatchee Watershed in Alabama, USA, in Response to Climate Change." Air, Soil and Water Research 14 (January 2021): 117862212199178. http://dx.doi.org/10.1177/1178622121991789.
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Climate change will ultimately result in higher surface temperature and more variable precipitation, negatively affecting agricultural productivity. To sustain the agricultural production in the face of climate change, adaptive agricultural management or best management practices (BMPs) are needed. The currently practiced BMPs include crop rotation, early planting, conservation tillage, cover crops, effective fertilizer use, and so on. This research investigated the agricultural production of BMPs in response to climate change for a Hydrologic Unit Code12 sub-watershed of Choctawhatchee Watershed in Alabama, USA. The dominating soil type of this region was sandy loam and loamy sand soil. Agricultural Production Systems sIMulator and Cropping Systems Simulation Model were used to estimate the agricultural production. Representative Concentration Pathway (RCP) 4.5 and RCP8.5 that projected a temperature increase of 2.3℃ and 4.7℃ were used as climate scenarios. The research demonstrated that crop rotation had positive response to climate change. With peanuts in the rotation, a production increase of 105% was observed for cotton. There was no consistent impact on crop yields by early planting. With selected peanut-cotton rotations, 50% reduced nitrogen fertilizer use was observed to achieve comparable crop yields. In response to climate change, crop rotation with legume incorporation is thus suggested, which increased crop production and reduced fertilizer use.
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Chatterjee, Amitava, and Saseendran S. Anapalli. "Comparing CSM-CROPGRO and APSIM-OzCot Simulations for Cotton Production and Eddy Covariance-Based Evapotranspiration in Mississippi." Water 14, no. 24 (December 9, 2022): 4022. http://dx.doi.org/10.3390/w14244022.
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Optimizing irrigation water use efficiency (WUE) is critical to reduce the dependency of irrigated cotton (Gossypium spp.) production on depleting aquifers. Cropping system models can integrate and synthesize data collected through experiments in the past and simulate management changes for enhancing WUE in agriculture. This study evaluated the simulation of cotton growth and evapotranspiration (ET) in a grower’s field using the CSM-CROPGRO-cotton module within the Decision Support System for Agrotechnology Transfer (DSSAT) and APSIM (Agricultural Production Systems simulator)-OzCot during 2017–2018 growing seasons. Crop ET was quantified using the eddy covariance (EC) method. Data collected in 2017 was used in calibrating the models and in 2018 validating. Over two cropping seasons, the simulated seedling emergence, flowering, and maturity dates were varied less than a week from measured for both models. Simulated leaf area index (LAI) varied from measured with the relative root mean squared errors (RRMSE) ranging between 20.6% to 38.7%. Daily ET deviated from EC estimates with root mean square errors (RMSEs) of 1.90 mm and 2.03 mm (RRMSEs of 63.1% and 54.8%) for the DSSAT and 1.95 mm and 2.17 mm (RRMSEs of 64.7% and 58.8%) for APSIM, during 2017 and 2018, respectively. Model performance varied with growing seasons, indicating improving ET simulation processes and long-term calibrations and validations are necessary for adapting the models for decision support in optimizing WUE in cotton cropping systems.
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Schwab, Charles V., Gretchen A. Mosher, and Saxon J. Ryan. "Agricultural Worker Injury Comparative Risk Assessment Methodology: Assessing Corn and Biofuel Switchgrass Production Systems." Journal of Agricultural Safety and Health 23, no. 3 (2017): 219–35. http://dx.doi.org/10.13031/jash.12245.
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Abstract. Keeping workers safe is a continuing challenge in agricultural production. Risk assessment methodologies have been used widely in other industries to better understand systems and enhance decision making, yet their use in production agriculture has been limited. This article describes the considerations and the approach taken to measure the difference in worker injury risks between two agricultural production systems. A model was developed specifically for the comparison of worker injury risk between corn and biofuel switchgrass production systems. The model is composed of injury and exposure values that were used in a Monte Carlo simulation. The output of this risk assessment shows that approximately 99% of the values from the Monte Carlo simulation rank corn production as a greater worker injury risk than biofuel switchgrass production. Furthermore, the greatest contributing factors for each production system were identified as harvest, and that finding aligns with current literature. Keywords: Agricultural production, Agricultural risk assessment, Agricultural safety, Biofuel production safety, Exposure distribution, Farm safety, Human safety risk assessment, Injury distribution, Monte Carlo, Regional risk assessment.
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Bartel, C. A., S. V. Archontoulis, A. W. Lenssen, K. J. Moore, I. L. Huber, D. A. Laird, and P. M. Dixon. "Modeling perennial groundcover effects on annual maize grain crop growth with the Agricultural Production Systems sIMulator." Agronomy Journal 112, no. 3 (March 18, 2020): 1895–910. http://dx.doi.org/10.1002/agj2.20108.
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Farré, Imma, Michael J. Robertson, Senthold Asseng, Robert J. French, and Miles Dracup. "Simulating lupin development, growth, and yield in a Mediterranean environment." Australian Journal of Agricultural Research 55, no. 8 (2004): 863. http://dx.doi.org/10.1071/ar04027.
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Simulation of narrow-leafed lupin (Lupinus angustifolius L.) production would be a useful tool for assessing agronomic and management options for the crop. This paper reports on the development and testing of a model of lupin development and growth, designed for use in the cropping systems simulator, APSIM (Agricultural Production Systems Simulator). Parameters describing leaf area expansion, phenology, radiation interception, biomass accumulation and partitioning, water use, and nitrogen accumulation were obtained from the literature or derived from field experiments. The model was developed and tested using data from experiments including different locations, cultivars, sowing dates, soil types, and water supplies. Flowering dates ranged from 71 to 109 days after sowing and were predicted by the model with a root mean square deviation (RMSD) of 4–5 days. Observed grain yields ranged from 0.5 to 2.7 t/ha and were simulated by the model with a RMSD of 0.5 t/ha. Simulation of a waterlogging effect on photosynthesis improved the model performance for leaf area index (LAI), biomass, and yield. The effect of variable rainfall in Western Australia and sowing date on yield was analysed using the model and historical weather data. Yield reductions were found with delay in sowing, particularly in water-limited environments. The model can be used for assessing some agronomic and management options and quantifying potential yields for specific locations, soil types, and sowing dates in Western Australia.
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Patrick Smith, F., Dean P. Holzworth, and Michael J. Robertson. "Linking icon-based models to code-based models: a case study with the agricultural production systems simulator." Agricultural Systems 83, no. 2 (February 2005): 135–51. http://dx.doi.org/10.1016/j.agsy.2004.03.004.
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Prada, Urbano Eliécer Gómez. "SARA: Videojuego Para el Aprendizaje Agrícola Soportado en Dinámica de Sistemas (DS)." KnE Engineering 3, no. 1 (February 11, 2018): 232. http://dx.doi.org/10.18502/keg.v3i1.1428.
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SARA is a videogame for the learning of agricultural production systems, in which the player simulates the decisions that would take in a greenhouse and concentrate them in a web portal for its subsequent follow-up, traceability and contrast of results in a way that favors the learning process. The game rules are supported in a simulation model elaborated with Systems Dynamics (SD).Keywords: Serious Videogame, Learning, Agriculture, Systems Dynamics, Web Information System, Knowledge Representation
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Paydar, Zahra, Neil Huth, Anthony Ringrose-Voase, Rick Young, Tony Bernardi, Brian Keating, and Hamish Cresswell. "Deep drainage and land use systems. Model verification and systems comparison." Australian Journal of Agricultural Research 56, no. 9 (2005): 995. http://dx.doi.org/10.1071/ar04303.
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Deep drainage or drainage below the bottom of the profile usually occurs when rain infiltrates moist soil with insufficient capacity to store the additional water. This drainage is believed to be contributing to watertable rise and salinity in some parts of the Liverpool Plains catchment in northern New South Wales. The effect of land use on deep drainage was investigated by comparing the traditional long fallow system with more intense ‘opportunity cropping’. Long fallowing (2 crops in 3 years) is used to store rainfall in the soil profile but risks substantial deep drainage. Opportunity cropping seeks to lessen this risk by sowing whenever there is sufficient soil moisture. Elements of the water balance and productivity were measured under various farming systems in a field experiment for 4 years in the southern part of the catchment. The experimental results were used to verify APSIM (Agricultural Production Systems Simulator) by comparing them with predictions of production, water storage, and runoff. The verification procedure also involved local farmers and agronomists who assessed the credibility of the predictions and suggested modifications. APSIM provided a realistic simulation of common farming systems in the region and could capture the main hydrological and biological processes. APSIM was then used for long-term (41 years) simulations to predict deep drainage under different systems and extrapolate experimental results. The results showed large differences between agricultural systems mostly because differences in evapotranspiration contributed to differences in profile moisture when it rained. The model predicted that traditional long fallow farming systems (2 crops in 3 years) are quite ‘leaky’, with average annual deep drainage of 34 mm. However, by planting crops in response to the depth of moist soil (opportunity or response cropping), APSIM predicted a much smaller annual drainage rate of 6 mm. Opportunity cropping resulted in overall greater water use and increased production compared with long fallowing. Furthermore, modelling indicated that average annual deep drainage under continuous sorghum (3 mm) is much less than under either long fallow cropping or continuous wheat (39 mm), demonstrating the importance of including summer cropping, as well as increasing cropping frequency, to reducing deep drainage.
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Guo, Tianting, Chunwei Liu, Ying Xiang, Pei Zhang, and Ranghui Wang. "Simulations of the Soil Evaporation and Crop Transpiration Beneath a Maize Crop Canopy in a Humid Area." Water 13, no. 14 (July 19, 2021): 1975. http://dx.doi.org/10.3390/w13141975.
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Soil evaporation (Es) and crop transpiration (Tc) are important components of water balance in cropping systems. Comparing the accurate calculation by crop models of Es and Tc to the measured evaporation and transpiration has significant advances to the optimal configuration of water resource and evaluation of the accuracy of crop models in estimating water consumption. To evaluate the adaptation of APSIM (Agricultural Production Systems simulator) in calculating the Es and Tc in Nanjing, APSIM model parameters, including the meteorological and soil parameters, were measured from a two-year field experiment. The results showed that: (1) The simulated evaporation was basically consistent with the measured Es, and the regulated model can effectively present the field evaporation in the whole maize growth period (R2 = 0.85, D = 0.96, p < 0.001); and (2) The trend of the simulated Tc can present the actual Tc variation, but the accuracy was not as high as the evaporation (R2 = 0.74, D = 0.87, p < 0.001), therefore, the simulation of water balance process by APSIM will be helpful in calculating Es and Tc in a humid area of Nanjing, and its application also could predict the production of maize fields in Nanjing.
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ANTLE, JOHN M., and JETSE J. STOORVOGEL. "Incorporating systems dynamics and spatial heterogeneity in integrated assessment of agricultural production systems." Environment and Development Economics 11, no. 1 (January 30, 2006): 39–58. http://dx.doi.org/10.1017/s1355770x05002639.
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Agricultural systems are complex and dynamic, being made up of inter-acting bio-physical and human sub-systems. Moreover, agricultural systems are re-markably diverse, both within geographic regions and across regions. Accordingly, this paper focuses on dynamics and heterogeneity in coupled, multi-disciplinary simulation models of agricultural systems. We begin with a discussion of the principal features of agricultural production systems. We then present an example of a ‘loosely coupled’ model, the type of model most researchers have used to represent agricultural systems. We discuss the loosely coupled model's features and limitations, and show how it can be modified to incorporate feedbacks among sub-models. Finally, we use a case study of a hillside production system in Ecuador to illustrate the importance of model coupling, dynamics and heterogeneity in the analysis of production systems. This example shows that feedbacks and threshold effects are most important at sites most vulnerable to tillage erosion.
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Foale, M. A., M. E. Probert, P. S. Carberry, D. Lack, S. Yeates, D. Brimblecombe, R.Shaw, and M. Crocker. "Participatory research in dryland cropping systems — monitoring and simulation of soil water and nitrogen in farmers' paddocks in Central Queensland." Australian Journal of Experimental Agriculture 44, no. 3 (2004): 321. http://dx.doi.org/10.1071/ea02205.
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Collaboration of researchers and service-providers with farmers in addressing crop and soil management, using on-farm experiments and cropping system simulation, was negotiated in 2 districts in Central Queensland, Australia. The 2 most influential variables affecting crop productivity in this region (soil water and mineral nitrogen contents) and the growth of sown crops, were monitored and simulated for 3 years beginning in December 1992. Periodic soil sampling of large experimental strips on 3 farms, from paddocks that differed in cropping history and soil properties, provided robust datasets of change, over time, of soil water and mineral nitrogen status. Farmers participated in twice-yearly discussions with researchers, informed by the accumulating data, which influenced thinking about soil behaviour and possible new management strategies. As the study period coincided with a prolonged drought, so that cropping opportunities were few, the objectives of the work were modified to concentrate almost exclusively on the soil variables.The contribution of the Agricultural Production Systems Simulator, which was used to simulate the measured changes in soil water and mineral nitrogen, was found by all participants to be useful. The APSIM output generally demonstrated close correspondence with field observations, which raised confidence in its applicability to local cropping systems. Exploration of hypothetical situations of interest to farmer participants, in the form of what-if scenarios, provided insights into the behaviour of the production system for a range of soil and seasonal conditions. The informed speculation of the simulator became a substitute for the farmers' own, more tentative, efforts.The regular participative review sessions proved to be highly effective in stimulating the learning of both farmers and researchers. The farmers were able to feel comfortable as owners of the collaborative experiments and custodians of the learning environment. Clear evidence for the ongoing learning of these farmers appeared in post-collaboration practices and experiences.
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Khan, Muhammad Aamir, Alishba Tahir, Nabila Khurshid, Muhammad Iftikhar ul Husnain, Mukhtar Ahmed, and Houcine Boughanmi. "Economic Effects of Climate Change-Induced Loss of Agricultural Production by 2050: A Case Study of Pakistan." Sustainability 12, no. 3 (February 7, 2020): 1216. http://dx.doi.org/10.3390/su12031216.
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This research combined global climate, crop and economic models to examine the economic impact of climate change-induced loss of agricultural productivity in Pakistan. Previous studies conducted systematic model inter-comparisons, but results varied widely due to differences in model approaches, research scenarios and input data. This paper extends that analysis in the case of Pakistan by taking yield decline output of the Decision Support System for Agrotechnology Transfer (DSSAT) for CERES-Wheat, CERES-Rice and Agricultural Production Systems Simulator (APSIM) crop models as an input in the global economic model to evaluate the economic effects of climate change-induced loss of crop production by 2050. Results showed that climate change-induced loss of wheat and rice crop production by 2050 is 19.5 billion dollars on Pakistan’s Real Gross Domestic Product coupled with an increase in commodity prices followed by a notable decrease in domestic private consumption. However, the decline in the crops’ production not only affects the economic agents involved in the agriculture sector of the country, but it also has a multiplier effect on industrial and business sectors. A huge rise in commodity prices will create a great challenge for the livelihood of the whole country, especially for urban households. It is recommended that the government should have a sound agricultural policy that can play a role in influencing its ability to adapt successfully to climate change as adaption is necessary for high production and net returns of the farm output.
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Anser, Muhammad Khalid, Tayyaba Hina, Shahzad Hameed, Muhammad Hamid Nasir, Ishfaq Ahmad, and Muhammad Asad ur Rehman Naseer. "Modeling Adaptation Strategies against Climate Change Impacts in Integrated Rice-Wheat Agricultural Production System of Pakistan." International Journal of Environmental Research and Public Health 17, no. 7 (April 7, 2020): 2522. http://dx.doi.org/10.3390/ijerph17072522.
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There are numerous anticipated effects of climate change (CC) on agriculture in the developing and the developed world. Pakistan is among the top ten most prone nations to CC in the world. The objective of this analysis was to quantify the economic impacts of CC on the agricultural production system and to quantify the impacts of suggested adaptation strategies at the farm level. The study was conducted in the Punjab province’s rice-wheat cropping system. For this purpose, climate modeling was carried out by using two representative concentration pathways (RCPs), i.e., RCPs 4.5 and 8.5, and five global circulation models (GCMs). The crop modeling was carried out by using the Agricultural Production Systems Simulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation models (CSMs), which were tested on the cross-sectional data of 217 farm households collected from the seven strata in the study area. The socio-economic impacts were calculated using the Multidimensional Impact Assessment Tradeoff Analysis Model (TOA-MD). The results revealed that CC’s net economic impact using both RCPs and CSMs was negative. In both CSMs, the poverty status was higher in RCP 8.5 than in RCP 4.5. The adaptation package showed positive results in poverty reduction and improvement in the livelihood conditions of the agricultural households. The adoption rate for DSSAT was about 78%, and for APSIM, it was about 68%. The adaptation benefits observed in DSSAT were higher than in APSIM. The results showed that the suggested adaptations could have a significant impact on the resilience of the atmospheric changes. Therefore, without these adaptation measures, i.e., increase in sowing density, improved cultivars, increase in nitrogen use, and fertigation, there would be negative impacts of CC that would capitalize on livelihood and food security in the study area.
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Hill, J. O., M. J. Robertson, B. C. Pengelly, A. M. Whitbread, and C. A. Hall. "Simulation modelling of lablab (Lablab purpureus) pastures in northern Australia." Australian Journal of Agricultural Research 57, no. 4 (2006): 389. http://dx.doi.org/10.1071/ar05263.
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The capability to simulate lablab production across a range of environments in northern Australia provides a useful tool for exploring agronomic and management options and risk assessments for the crop. This paper reports on the development and testing of a model of lablab (annual cultivar cv. Highworth and perennial cultivar cv. Endurance) growth, designed for use in the cropping systems simulator, APSIM (Agricultural Production Systems Simulator). Parameters describing leaf area expansion, biomass accumulation, and partitioning were derived from field experiments, and other essential parameters were assumed from similar tropically adapted legumes. The model was tested against data from experiments including different locations, cultivars, sowing dates, soil types, and water availability. Observed biomass ranged from 63 to 13055 kg dry matter/ha and was predicted by the model in an independent test with a root mean square deviation of 770 kg dry matter/ha. Observed time courses of biomass production for both the annual and perennial cultivars were reproduced well, as was the partitioning of biomass into leaves and stems. The effect of variable rainfall and temperature in northern Australia was analysed using the model and historical climate data. Yield reductions were found in the more inland and southern-most parts of the region where summer rainfall and/or temperatures are lower.
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Ribeiro, João P. L., Pedro D. Gaspar, Vasco N. G. J. Soares, and João M. L. P. Caldeira. "Computational Simulation of an Agricultural Robotic Rover for Weed Control and Fallen Fruit Collection—Algorithms for Image Detection and Recognition and Systems Control, Regulation, and Command." Electronics 11, no. 5 (March 3, 2022): 790. http://dx.doi.org/10.3390/electronics11050790.
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The continuous rise in the world’s population has increased the need for food, resulting in a rise of agricultural holdings to ensure the supply of these goods directly to the populations and indirectly to all processing industries in the food business. This situation has led agriculture to reinvent itself and introduce new technics and tools to ensure tighter control of the crops and increase yields in food production. However, the lack of labor coupled with the evolution of weeds resistant to herbicides created a crisis in agricultural food production. However, with the growing evolution in electronics, automation, and robotics, new paths are emerging to solve these problems. A robotic rover was designed to optimize the tasks of weed control and collection of fallen fruits of an orchard. In weed control, a localized spraying system is proposed, therefore reducing the amount of applied herbicides. With fruit collection, it is possible to direct fallen fruits for animal feeding and possible to reduce microbial activity on the next campaign crops, therefore avoiding damage. This study proposes the simulation of this robotic rover on robotic simulation software. It also proposes the replication of a similar environment of an orchard to generate an algorithm that controls the rover on the tasks of localized spraying and fallen fruit collection. Creating and testing these algorithms by using a robotic simulator speed up and ease the evaluation of different scenarios and hypotheses, with the added benefit of being able to test two tasks simultaneously. This method also allows greater freedom and creativity because there are no concerns about hardware damage. It should also be noted that development costs are very low.
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Stockle, C. O., R. I. Papendick, K. E. Saxton, G. S. Campbell, and F. K. van Evert. "A framework for evaluating the sustainability of agricultural production systems." American Journal of Alternative Agriculture 9, no. 1-2 (June 1994): 45–50. http://dx.doi.org/10.1017/s0889189300005555.
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AbstractSustainable agriculture has gained acceptance as a conceptual approach for shaping farming systems of the future. All definitions of sustainable agriculture include food productivity, food safety, resource protection, quality of life and environmental quality. However, the sustainability of a wide range of farming systems has been judged only subjectively. Currently there are no scientific criteria to evaluate the sustainability of a specific farming system. We propose a framework for evaluating the relative sustainability of a farming system using nine attributes: profitability, productivity, soil quality, water quality, air quality, energy efficiency, fish and wildlife habitat, quality of life, and social acceptance. Each attribute is scored and then weighted in a way that is subjective and dependent on the judgment of the evaluating team, but that must be expressed numerically. The scoring must be based on quantifiable constraints within each attribute. Constraints can be quantified by direct measurement, which is already true for those related to profitability, productivity, water quality and energy efficiency. Constraints that are not readily measurable will need other evaluation techniques, including expert opinion and computer simulation models.
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Dilla, Aynalem M., Philip J. Smethurst, Neil I. Huth, and Karen M. Barry. "Plot-Scale Agroforestry Modeling Explores Tree Pruning and Fertilizer Interactions for Maize Production in a Faidherbia Parkland." Forests 11, no. 11 (November 4, 2020): 1175. http://dx.doi.org/10.3390/f11111175.
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Poor agricultural productivity has led to food shortages for smallholder farmers in Ethiopia. Agroforestry may improve food security by increasing soil fertility, crop production, and livelihoods. Agroforestry simulation models can be useful for predicting the effects of tree management on crop growth when designing modifications to these systems. The Agricultural Production Systems sIMulator (APSIM) agroforestry tree-proxy model was used to simulate the response of maize yield to N fertilizer applications and tree pruning practices in the parkland agroforestry system in the Central Rift Valley, Ethiopia. The model was parameterized and tested using data collected from an experiment conducted under trees and in crop-only plots during the 2015 and 2016 growing seasons. The treatments contained three levels of tree pruning (100% pruned, 50% pruned, and unpruned) as the main plots, and N fertilizers were applied to maize at two rates (9 or 78 kg N ha−1) as sub-plots. Maize yield predictions across two years in response to tree pruning and N applications under tree canopies were satisfactorily simulated (NSE = 0.72, RSR = 0.51, R2 = 0.8). Virtual experiments for different rates of N, pruning levels, sowing dates, and cultivars suggest that maize yield could be improved by applying fertilizers (particularly on crop-only plots) and by at least 50% pruning of trees. Optimal maize yield can be obtained at a higher rate of fertilization under trees than away from them due to better water relations, and there is scope for improving the sowing date and cultivar. Across a 34-year range of recent climate, small increases in yields due to optimum N-fertilizing and pruning were probably limited by nutrient limitations other than N, but the highest yields were consistently in the 2–4 m zone under trees. These virtual experiments helped to form hypotheses regarding fertilizers, pruning, and the effects of trees on soil that warrant further field evaluation.
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Touch, Van, De Li Liu, Robert John Martin, Jeannette Fiona Scott, Annette Cowie, and Daniel K. Y. Tan. "Building Farming Resilience to Climate Change: Upland Crop Production in Northwest Cambodia." Proceedings 36, no. 1 (April 7, 2020): 157. http://dx.doi.org/10.3390/proceedings2019036157.
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Production of upland crops such as maize, cassava, soybean, mungbean, peanut and sesame contribute importantly to Cambodia’s economy and food security, especially for those who live in the upland areas found in almost every province of Cambodia. The upland farmers are highly vulnerable to climate variability and climate change due to low adaptive capacity and high dependence on rainfed crop production for their livelihoods. This study involved in-depth review of literature, conducting on-farm experiments, downscaling climate projections from the coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs), running Agricultural Production Systems sIMulator (APSIM) simulations and farmer consultation to define climate impacts and explore adaptation options that could build resilience to the existing and projected climate change scenarios for upland cropping farmers in Northwest Cambodia. Insufficient water and nutrient depletion were the main production risks and yield limiting factors. On-farm adaptation options such as modifying sowing windows, including legumes in crop rotations and additional fertiliser application are likely to substantially minimise risks from climatic impacts, and increase and sustain returns. Wider adoption of conservation agriculture practices—including reduced tillage and crop residue retention, that enhance soil structure and soil water holding capacity and reduce soil erosion, should enhance productivity and incomes, while making the farming systems more resilient to the existing and projected climate variability and climate change, and other production stressors.
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Hasan, MK, S. Akhter, MAH Chowdhury, AK Chaki, MRA Chawdhery, and T. Zahan. "Prediction of changing climatic effect and risk management by using simulation approaches for rice-wheat system in Bangladesh." Bangladesh Journal of Agricultural Research 44, no. 2 (June 16, 2019): 311–26. http://dx.doi.org/10.3329/bjar.v44i2.41820.
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A study was carried out on the impact of climate change in rice-wheat systems on farmers’ livelihood in Dinajpur region of Bangladesh to evaluate the usefulness of the implication of simulation approaches to predict climate change effect and to manage risk for this cropping system. Trade-off analysis for multidimensional impact assessment (TOA-MD) model was used in the study with a combination of simulated baseline production and future simulated yield using Decision Support Systems for Agro-technology Transfer (DSSAT) and Agricultural Production Systems SIMulator (APSIM) in rice and wheat production system. Five different climate scenarios of Global Circulation Models (GCMs) were considered. The projections showed to have a negative economic impact between 50 and 82% for the difference in the magnitude and in the impact of different GCMs which was not possible to overcome. The survey revealed that northwest region of Bangladesh is likely to be affected by climate change and has high levels of vulnerability due to limited access to alternative livelihood activities other than farming. Simulation results showed no additional economic gain from wheat cultivation under changed climatic conditions, but increased economic profit was obtained from rice cultivation due to increased productivity trend. Therefore, study suggests an adaptation package of 50 mm additional irrigation water for wheat cultivation that could be an appropriate strategy to mitigate climate change risk in wheat cultivation. This practice had a positive impact on projected per capita income gains of about 2.05%in the study area and reduced poverty rate by about 1.99%. The study also revealed that prediction of the APSIM model for adaptation impact of climate change on economic return and per capita income of farmers was superior to DSSAT model.
Bangladesh J. Agril. Res. 44(2): 311-326, June 2019
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La Mantia, Francesco Paolo, Manuela Ceraulo, Paolo Testa, and Marco Morreale. "Biodegradable Polymers for the Production of Nets for Agricultural Product Packaging." Materials 14, no. 2 (January 9, 2021): 323. http://dx.doi.org/10.3390/ma14020323.
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It is well known that the need for more environmentally friendly materials concerns, among other fields, the food packaging industry. This regards also, for instance, nets used for agricultural product (e.g., citrus fruits, potatoes) packaging. These nets are typically manufactured by film blowing technique, with subsequent slicing of the films and cold drawing of the obtained strips, made from traditional, non-biodegradable polymer systems. In this work, two biodegradable polymer systems were characterized from rheological, processability, and mechanical points of view, in order to evaluate their suitability to replace polyethylene-based polymer systems typically used for agricultural product net manufacturing. Furthermore, laboratory simulation of the above-mentioned processing operation paths was performed. The results indicated a good potential for biodegradable polymer systems to replace polyethylene-based systems for agricultural product packaging.
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La Mantia, Francesco Paolo, Manuela Ceraulo, Paolo Testa, and Marco Morreale. "Biodegradable Polymers for the Production of Nets for Agricultural Product Packaging." Materials 14, no. 2 (January 9, 2021): 323. http://dx.doi.org/10.3390/ma14020323.
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It is well known that the need for more environmentally friendly materials concerns, among other fields, the food packaging industry. This regards also, for instance, nets used for agricultural product (e.g., citrus fruits, potatoes) packaging. These nets are typically manufactured by film blowing technique, with subsequent slicing of the films and cold drawing of the obtained strips, made from traditional, non-biodegradable polymer systems. In this work, two biodegradable polymer systems were characterized from rheological, processability, and mechanical points of view, in order to evaluate their suitability to replace polyethylene-based polymer systems typically used for agricultural product net manufacturing. Furthermore, laboratory simulation of the above-mentioned processing operation paths was performed. The results indicated a good potential for biodegradable polymer systems to replace polyethylene-based systems for agricultural product packaging.
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Li, Hongjun, Zhijun Liu, Li Zheng, and Yuping Lei. "Resilience analysis for agricultural systems of north China plain based on a dynamic system model." Scientia Agricola 68, no. 1 (February 2011): 8–17. http://dx.doi.org/10.1590/s0103-90162011000100002.
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This paper presents a case study of resilience theory to understand agricultural systems in the Luancheng County of the North China Plain. A dynanic system model coupled with social, economic and ecological subsystems of agriculture for the Luancheng County was constructed with a time step of one month. The model includes five main components, water resources, profitability, irrigation, crop yield and area. The simulated groundwater table, wheat area and yield, maize area and yield, and rural labor transfer reflected the general trend of the observed data, with calculated determination coefficients higher than 0.88. Resilience of agricultural systems, as indicated by agricultural profitability and food security, were explored for the Luancheng County. Initially, investments in agriculture increased its resilience rapidly. However, with the degradation of resources and the increases in agricultural investment, the cost of agricultural production became too high to gain profit. The rise in population increases the risk of food security. As a result, the resilience of agricultural systems decreased gradually. The Luancheng County is now in the conservation phase of the adaptive cycle. Partial adjustments should be introduced to enhance its resilience and promote the continuing development of the agricultural systems.
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Bosire, Emily, Fredrick Karanja, Gilbert Ouma, and Wilson Gitau. "Assessment of Climate Change Impact on Sorghum Production in Machakos County." Sustainable Food Production 3 (November 2018): 25–45. http://dx.doi.org/10.18052/www.scipress.com/sfp.3.25.
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The APSIM (Agricultural Production Systems sIMulator) model was used to assess the impact of climate change on sorghum production in the semi arid low lands of Machakos County under three future scenarios of climate change (2010-2039, 2040-2069 and 2070-2099) using two Representative Concentration Pathways (RCPs): RCP 4.5 and RCP 8.5. The APSIM model was calibrated and evaluated using field experimental data obtained from a two-year experiment (2014 to 2015) of sorghum parameters carried out at Kenya Agricultural and Livestock Research Organization (KALRO) in Katumani. Model evaluation shows that APSIM sorghum model was capable in quantifying the response of sorghum to nitrogen (N). The values of root mean square error obtained were low for all the sorghum parameters studied. Higher values of modified index of agreement showed more precise simulation of total biomass and grain yield. The observed and simulated sorghum parameters for both cultivars during the long and short growing seasons depicted good correlation with r2values ranging between 45 % and 99%. Across all the GCMs projected mean changes on phenological dates (days to 50% flowering and physiological maturity) showed a consistent decline for both sorghum varieties during the long and short growing seasons with the application of different rates of fertilizer. These trends were more manifested in the RCP8.5 than RCP4.5 and in the end century (2071-2100) of the simulation. With the RCP8.5 flowering dates reduced by 24 and 28 days and the crop cycle duration shrinked by 35 and 38 days in the end century (2071-2100) for gadam and seredo, respectively. There was slight increase or decrease in biomass for both varieties under climate change with no fertilizer application. However, with application of 50kgha-1N, there was a slight increase of biomass. It has been noted that under changing climate sorghum grain yields will constantly increase for both cultivars over the three future time periods with almost 85.3% increase as we approach the end of the century (2070-2099). The extent of yield change was higher for seredo than for gadam.
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Иваньо, Yaroslav Ivano, Бендик, and Nadezhda Bendik. "SOFTWARE OF MODELLING OF AGRICULTURAL PARAMETERS TAKING INTO ACCOUNT NATURAL AND TECHNOGENIC EVENTS." Vestnik of Kazan State Agrarian University 10, no. 3 (September 15, 2015): 67–71. http://dx.doi.org/10.12737/14759.
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The article is considered the developed software of Informatics and mathematical modelling Department of Irkutsk State Agricultural Unievrsity. The scheme of software tools interaction is designed to optimize their performance. Software systems are grouped on the basis of three groups. The first group of software includes software systems: “Regional agriculture cluster”, “Optimization of land resources usage” and “Environmental and mathematical modeling of food production”. It allows to plan the production of agricultural products. The second group is selected on the basis of the planning of agricultural production, taking into account natural, technogenic events and its combinations. This group includes the following software packages: “Drought’, “Natural disasters”, “Unusual occurrence and technogenic events”. The third group is focused on yield planning in a timely crop sowing. The group includes software package: “Simulation of crops bio-productivity” and information system “Predicting the time of technological operations and planning”. These developed group of software tools combined in software and hardware management of agricultural production on the basis of the data warehouse and knowledge. The basis of hardware and software management of agricultural production is the back end, which includes data storage, workstations management system and communication system with navigation devices. The above-mentioned software groups act as a client application.
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Akinseye, Folorunso M., Hakeem A. Ajegbe, Alpha Y. Kamara, Elijah A. Adefisan, and Anthony M. Whitbread. "Understanding the response of sorghum cultivars to nitrogen applications in the semi-arid Nigeria using the agricultural production systems simulator." Journal of Plant Nutrition 43, no. 6 (January 16, 2020): 834–50. http://dx.doi.org/10.1080/01904167.2020.1711943.
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Huang, Jianhua, YanDang Chen, and Shan He. "The Evolutionary Analysis of Agricultural Production Transaction Under The Price Subsidy Policy." International Journal of Information Systems and Supply Chain Management 13, no. 1 (January 2020): 73–97. http://dx.doi.org/10.4018/ijisscm.2020010104.
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The scattered and small-scale production mode together with asymmetric business information results in the Chinese peasants' weak position in the agricultural supply chain. Chinese government has implemented some effective measures to safeguard the peasants' benefits. By establishing a tripartite evolutionary game model among the peasants, agricultural products dealers and government under the policy of price subsidy, the effects of social benefits, the size of the penalty and the transaction volume on the evolutionary stable strategy is discussed. A simulation instance is also given to demonstrate the evolutionary game model. The results shows that the probability of government regulation is not only related to the social benefits of regulation, but also affected by the transaction volume of agricultural products, the peasants benefits can be protected effectively by the price subsidy policy and the probability of the agricultural products dealers choosing fraud strategy declines with the increase of penalty and increases with the transaction volume of agricultural products.
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Moeller, Carina, Mustafa Pala, Ahmad M. Manschadi, Holger Meinke, and Joachim Sauerborn. "Assessing the sustainability of wheat-based cropping systems using APSIM: model parameterisation and evaluation." Australian Journal of Agricultural Research 58, no. 1 (2007): 75. http://dx.doi.org/10.1071/ar06186.
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Assessing the sustainability of crop and soil management practices in wheat-based rotations requires a well-tested model with the demonstrated ability to sensibly predict crop productivity and changes in the soil resource. The Agricultural Production Systems Simulator (APSIM) suite of models was parameterised and subsequently used to predict biomass production, yield, crop water and nitrogen (N) use, as well as long-term soil water and organic matter dynamics in wheat/chickpea systems at Tel Hadya, north-western Syria. The model satisfactorily simulated the productivity and water and N use of wheat and chickpea crops grown under different N and/or water supply levels in the 1998–99 and 1999–2000 experimental seasons. Analysis of soil-water dynamics showed that the 2-stage soil evaporation model in APSIM’s cascading water-balance module did not sufficiently explain the actual soil drying following crop harvest under conditions where unused water remained in the soil profile. This might have been related to evaporation from soil cracks in the montmorillonitic clay soil, a process not explicitly simulated by APSIM. Soil-water dynamics in wheat–fallow and wheat–chickpea rotations (1987–98) were nevertheless well simulated when the soil water content in 0–0.45 m soil depth was set to ‘air dry’ at the end of the growing season each year. The model satisfactorily simulated the amounts of NO3-N in the soil, whereas it underestimated the amounts of NH4-N. Ammonium fixation might be part of the soil mineral-N dynamics at the study site because montmorillonite is the major clay mineral. This process is not simulated by APSIM’s nitrogen module. APSIM was capable of predicting long-term trends (1985–98) in soil organic matter in wheat–fallow and wheat–chickpea rotations at Tel Hadya as reported in literature. Overall, results showed that the model is generic and mature enough to be extended to this set of environmental conditions and can therefore be applied to assess the sustainability of wheat–chickpea rotations at Tel Hadya.
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Bandara, W. B. M. A. C., Kazuhito Sakai, Tamotsu Nakandakari, Preecha Kapetch, Mitsumasa Anan, Shinya Nakamura, Hideki Setouchi, and R. H. K. Rathnappriya. "Global Optimization of Cultivar Trait Parameters in the Simulation of Sugarcane Phenology Using Gaussian Process Emulation." Agronomy 11, no. 7 (July 7, 2021): 1379. http://dx.doi.org/10.3390/agronomy11071379.
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The global optimization of parameters in process-based crop models is often considered computationally expensive. Gaussian process (GP) emulation is a widely used method for reducing the computational burden of the optimization process. Total above-ground biomass and cane dry weight of three Thai sugarcane cultivars (KK3, LK92-11 and 02-2-058) collected under rainfed and irrigated conditions were used to optimize cultivar-specific parameters in the Agricultural Production Systems sIMulator (APSIM)-Sugarcane crop model through a GP emulation. GP emulators were trained and validated to approximate APSIM-Sugarcane model and then used for optimizing the cultivar-specific parameters through the differential evolution algorithm. Resulting optimized parameters allowed to obtain simulations that quite well approximated the observed biomass and CDW (validation results between simulated and observed yields: R2 0.93–0.98; normalized root mean squared error: 5–22%; Willmott’s agreement index: 0.87–0.99). The best parametrization was obtained under the lowest water stressed conditions. Based on these results, we suggest that GP emulation can be efficiently implemented for the parameterization of computationally expensive simulators.
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Pembleton, K. G., R. P. Rawnsley, J. L. Jacobs, F. J. Mickan, G. N. O'Brien, B. R. Cullen, and T. Ramilan. "Evaluating the accuracy of the Agricultural Production Systems Simulator (APSIM) simulating growth, development, and herbage nutritive characteristics of forage crops grown in the south-eastern dairy regions of Australia." Crop and Pasture Science 64, no. 2 (2013): 147. http://dx.doi.org/10.1071/cp12372.
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Pasture-based dairy farms are a complex system involving interactions between soils, pastures, forage crops, and livestock as well as the economic and social aspects of the business. Consequently, biophysical and farm systems models are becoming important tools to study pasture-based dairy systems. However, there is currently a paucity of modelling tools available for the simulation of one key component of the system—forage crops. This study evaluated the accuracy of the Agricultural Production Systems Simulator (APSIM) in simulating dry matter (DM) yield, phenology, and herbage nutritive characteristics of forage crops grown in the dairy regions of south-eastern Australia. Simulation results were compared with data for forage wheat (Triticum aestivum L.), oats (Avena sativa L.), forage rape (Brassica napus L.), forage sorghum (Sorghum bicolor (L.) Moench), and maize (Zea mays L.) collated from previous field research and demonstration activities undertaken across the dairy regions of south-eastern Australia. This study showed that APSIM adequately predicted the DM yield of forage crops, as evidenced by the range of values for the coefficient of determination (0.58–0.95), correlation coefficient (0.76–0.94), and bias correction factor (0.97–1.00). Crop phenology for maize, forage wheat, and oats was predicted with similar accuracy to forage crop DM yield, whereas the phenology of forage rape and forage sorghum was poorly predicted (R2 values 0.38 and 0.80, correlation coefficient 0.62 and –0.90, and bias correction factors 0.67 and 0.28, respectively). Herbage nutritive characteristics for all crop species were poorly predicted. While the selection of a model to explore an aspect of agricultural production will depend on the specific problem being addressed, the performance of APSIM in simulating forage crop DM yield and, in many cases, crop phenology, coupled with its ease of use, open access, and science-based mechanistic methods of simulating agricultural and crop processes, makes it an ideal model for exploring the influence of management and environment on forage crops grown on dairy farms in south-eastern Australia. Potential future model developments and improvements are discussed in the context of the results of this validation analysis.
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Stone, Roger C., and Holger Meinke. "Operational seasonal forecasting of crop performance." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2109–24. http://dx.doi.org/10.1098/rstb.2005.1753.
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Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.
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HIROOKA, Hiroyuki. "Systems approaches to beef cattle production systems using modeling and simulation." Animal Science Journal 81, no. 4 (May 13, 2010): 411–24. http://dx.doi.org/10.1111/j.1740-0929.2010.00769.x.
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Chaux, Jesus David, David Sanchez-Londono, and Giacomo Barbieri. "A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture." Applied Sciences 11, no. 19 (September 24, 2021): 8875. http://dx.doi.org/10.3390/app11198875.
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To ensure food security, agricultural production systems should innovate in the direction of increasing production while reducing utilized resources. Due to the higher level of automation with respect to traditional agricultural systems, Controlled Environment Agriculture (CEA) applications generally achieve better yields and quality crops at the expenses of higher energy consumption. In this context, Digital Twin (DT) may constitute a fundamental tool to reach the optimization of the productivity, intended as the ratio between production and resource consumption. For this reason, a DT Architecture for CEA systems is introduced within this work and applied to a case study for its validation. The proposed architecture is potentially able to optimize productivity since it utilizes simulation software that enables the optimization of: (i) Climate control strategies related to the control of the crop microclimate; (ii) treatments related to crop management. Due to the importance of food security in the worldwide landscape, the authors hope that this work may impulse the investigation of strategies for improving the productivity of CEA systems.
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Yang, Xuan, Dorothy Menefee, Song Cui, and Nithya Rajan. "Assessing the impacts of projected climate changes on maize (Zea mays) productivity using crop models and climate scenario simulation." Crop and Pasture Science 72, no. 12 (2021): 969. http://dx.doi.org/10.1071/cp21279.
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ContextInvestigating agronomic responses of dryland maize (Zea mays L.) systems under global change could provide important insights in designing climate-resilient cropping systems.Aims and methodsIn this study, we integrated Agricultural Production Systems sIMulator (APSIM) with Representative Concentration Pathways 8.5 and 20 Global Climate Models to systematically: (1) calibrate and validate APSIM using large-field study conducted in East-Central Texas; (2) evaluate the impacts of climate change on maize productivity and risks; and (3) investigate the variations in growth stage lengths.Key resultsResults indicated that APSIM simulated grain yield, biomass production, precipitation productivity (PP; kgha−1mm−1) and developmental stage transition agreed well with observation (NRMSE<14.9%). Changes in temperature and precipitation shortened growing seasons and affected available water, resulting in widely varied yield and PP. Mean grain yield changed from −34.8 to +19.7%, mean PP were improved 9.2–36.5%. The grain production could be maintained at least the standard of 75% of historical in most cases, but with greater risks for achieving higher threshold (50% of baseline). Finally, simulations indicated shortened days (4–13days) for reaching key developmental stages for maize.Conclusions and implicationsThe results advocate adoptions of management practice that incorporating early sowing, irrigations at sowing/VT stages, and selections of late-maturing cultivars for better sustainability and higher productivity.
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Nasirahmadi, Abozar, and Oliver Hensel. "Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm." Sensors 22, no. 2 (January 10, 2022): 498. http://dx.doi.org/10.3390/s22020498.
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Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.
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Mielenz, Henrike, Peter J. Thorburn, Robert H. Harris, Sally J. Officer, Guangdi Li, Graeme D. Schwenke, and Peter R. Grace. "Nitrous oxide emissions from grain production systems across a wide range of environmental conditions in eastern Australia." Soil Research 54, no. 5 (2016): 659. http://dx.doi.org/10.1071/sr15376.
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Nitrous oxide (N2O) emissions from Australian grain cropping systems are highly variable due to the large variations in soil and climate conditions and management practices under which crops are grown. Agricultural soils contribute 55% of national N2O emissions, and therefore mitigation of these emissions is important. In the present study, we explored N2O emissions, yield and emissions intensity in a range of management practices in grain crops across eastern Australia with the Agricultural Production Systems sIMulator (APSIM). The model was initially evaluated against experiments conducted at six field sites across major grain-growing regions in eastern Australia. Measured yields for all crops used in the experiments (wheat, barley, sorghum, maize, cotton, canola and chickpea) and seasonal N2O emissions were satisfactorily predicted with R2=0.93 and R2=0.91 respectively. As expected, N2O emissions and emissions intensity increased with increasing nitrogen (N) fertiliser input, whereas crop yields increased until a yield plateau was reached at a site- and crop-specific N rate. The mitigation potential of splitting N fertiliser application depended on the climate conditions and was found to be relevant only in the southern grain-growing region, where most rainfall occurs during the cropping season. Growing grain legumes in rotation with cereal crops has great potential to reduce mineral N fertiliser requirements and so N2O emissions. In general, N management strategies that maximise yields and increase N use efficiency showed the greatest promise for N2O mitigation.
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Chauhan, Yash, Graeme Wright, Nageswara Rao Rachaputi, Stephen Krosch, Michael Robertson, John Hargreaves, and Alan Broome. "Using APSIM-soiltemp to simulate soil temperature in the podding zone of peanut." Australian Journal of Experimental Agriculture 47, no. 8 (2007): 992. http://dx.doi.org/10.1071/ea06137.
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Measurement or accurate simulation of soil temperature is important for improved understanding and management of peanuts (Arachis hypogaea L.), due to their geocarpic habit. A module of the Agricultural Production Systems Simulator Model (APSIM), APSIM-soiltemp, which uses input of ambient temperature, rainfall and solar radiation in conjunction with other APSIM modules, was evaluated for its ability to simulate surface 5 cm soil temperature in 35 peanut on-farm trials conducted between 2001 and 2005 in the Burnett region (25°36′S to 26°41′S, 151°39′E to 151°53′E). Soil temperature simulated by the APSIM-soiltemp module, from 30 days after sowing until maturity, closely matched the measured values (R2 ≥ 0.80) in the first three seasons (2001–04). However, a slightly poorer relationship (R2 = 0.55) between the observed and the simulated temperatures was observed in 2004–05, when the crop was severely water stressed. Nevertheless, over all the four seasons, which were characterised by a range of ambient temperature, leaf area index, radiation and soil water, each of which was found to have significant effects on soil temperature, a close 1 : 1 relationship (R2 = 0.85) between measured and simulated soil temperatures was observed. Therefore, the pod zone soil temperature simulated by the module can be generally relied on in place of measured input of soil temperature in APSIM applications, such as quantifying climatic risk of aflatoxin accumulation.
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Lisson, S. N., N. J. Mendham, and P. S. Carberry. "Development of a hemp (Cannabis sativa L.) simulation model 4. Model description and validation." Australian Journal of Experimental Agriculture 40, no. 3 (2000): 425. http://dx.doi.org/10.1071/ea99061.
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In studies assessing the prospects for a hemp industry, as well as in longer term research activities, the use of a hemp simulation model to complement the more traditional agronomic field trials would offer a number of potential advantages. In addition to being cost and labour intensive, field trials with hemp have political, social and security implications. With these implications in mind, a simulation model that captures the growth and development processes of hemp in response to management, genotypic, soil and climate factors, has the potential to increase research efficiency. The model could be used to assess the need, extent and nature of field trials, to help interpret field trial results, and to investigate temporal and spatial variability in selected crop responses. This paper describes a hemp crop growth and development model (APSIM-Hemp) and its validation against an independent dataset. The model was developed as a crop module within the framework of the larger systems model, Agricultural Production Systems sIMulator (APSIM), to extend the capability to encompass the agricultural system in which hemp is grown. APSIM-Hemp incorporates relationships developed in the previous papers in this series relating to pre- and post-emergent phenology and leaf area production. Other parameters relating to biomass partitioning, biomass production, water uptake and nitrogen uptake were derived from separate field studies and selected references. APSIM-Hemp adequately predicted phenology, leaf area and biomass production for the cultivar Kompolti at Forthside in north-western Tasmania, for a dataset comprised of results from trials conducted over 3 seasons and including treatments of sowing date, irrigation regime and plant density. Although performing well against this independent dataset, the performance of the model needs to be further validated over a range of other soil, climate and management conditions in order to assess its broader predictive capability. Notwithstanding these limitations, the sound basis of a model for simulating the growth and development of hemp has been developed.
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Wu, Lu, Liping Feng, Yizhuo Li, Jing Wang, and Lianhai Wu. "A Yield-Related Agricultural Drought Index Reveals Spatio-Temporal Characteristics of Droughts in Southwestern China." Sustainability 11, no. 3 (January 29, 2019): 714. http://dx.doi.org/10.3390/su11030714.
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Southwestern China (SWC), one of the major rain-fed wheat production zones in China, has become vulnerable to drought in recent years under global climate change. To quantify drought severity during the wheat growing season and its impact on yield loss, we selected the Agricultural Production Systems sIMulator (APSIM) model to simulate wheat growth between 1961 and 2010 in SWC. A new drought index was developed considering different weighting factors of drought for yield loss in three growing phases. The index was shown to be reliable in assessing drought severity in the region. On average, an abnormal drought mainly occurred in mid-west Guizhou with a frequency of 10–30%. Central SWC was subjected to moderate drought with a frequency of 10–30%, whereas severe drought often occurred in Southern Sichuan and the middle of Yunnan with a frequency >50%. Temporally, drought severity fluctuated before 1990, but increased significantly afterwards. Our assessment suggested that irrigation during the period from floral initiation to flowering would help to ameliorate the effects of water stress under climatic variability in the region.
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Berzsenyi, Z. "Crop production research in multifunctional agriculture." Acta Agronomica Hungarica 51, no. 1 (April 1, 2003): 91–99. http://dx.doi.org/10.1556/aagr.51.2003.1.12.
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The research agenda for crop science in the 21st century will depend largely on whether the present conditions regarding the global food surplus continue, or whether a food scarcity recurs. Crop production research is based chiefly on small-plot field experiments, the majority of which are either long-term experiments or experiments set up to investigate the specific agronomic responses of Martonvásár maize hybrids and wheat varieties. The sustainability of crop production is examined in long-term experiments. The agronomic responses of maize hybrids and wheat varieties are studied at various levels of biological organisation. Growth analysis facilitates the exact characterisation of agronomic responses and the grouping of response effects and types using multivariable methods. Continued experimentation coupled with crop simulation models and decision support systems are an ever more useful framework for analysing the complexity of agricultural systems.
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Vogeler, I., and R. Cichota. "Deriving seasonally optimal nitrogen fertilization rates for a ryegrass pasture based on agricultural production systems simulator modelling with a refined AgPasture model." Grass and Forage Science 71, no. 3 (August 6, 2015): 353–65. http://dx.doi.org/10.1111/gfs.12181.
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Zhao, Panpan, Yang Zhou, Fengfeng Li, Xiaoxia Ling, Nanyan Deng, Shaobing Peng, and Jianguo Man. "The Adaptability of APSIM-Wheat Model in the Middle and Lower Reaches of the Yangtze River Plain of China: A Case Study of Winter Wheat in Hubei Province." Agronomy 10, no. 7 (July 8, 2020): 981. http://dx.doi.org/10.3390/agronomy10070981.
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The middle and lower reaches of the Yangtze River (MLYR) plain represent the second-largest wheat producing area in China; the winter wheat-rice system is one of the main planting systems in this region. The use of the agricultural production system simulator (APSIM)-wheat model to simulate wheat production potential and evaluate the impact of future climate change on wheat production in this region is of great importance. In this study, the adaptability of the APSIM-wheat model in the MLYR was evaluated based on observational data collected in field experiments and daily meteorological data from experimental stations in Wuhan, Jingmen, and Xiangyang in Hubei province. The results showed significant positive relationships between model-predicted wheat growth duration from sowing to anthesis and maturity and the observed values, with coefficients of determination (R2) in ranges of 0.90–0.97 and 0.93–0.96, respectively. The normalized root-mean-square error (NRMSE) of the simulated growth durations and measured values were lower than 1.6%, and the refined index of agreement (dr-values) was in the range of 0.74–0.87. The percent mean absolute relative error (PMARE) was cited here as a new index, with a value below 1.4%, indicating that the model’s rating was excellent. The model’s performance in terms of grain yield and above-ground biomass simulation was also acceptable, although it was not as good as the growth periods simulation. The R2 value was higher than 0.75 and 0.72 for the simulation of grain yield and biomass, respectively. The indices NRMSE and PMARE were lower than 19.8% and 19.9%, and the dr-value was higher than 0.71. According to our results, APSIM-wheat was an effective and accurate model for simulating the phenology and yield production processes of wheat in the MLYR, and the results also provided a theoretical basis and technical support for further research on the yield potential of wheat-rice rotation planting systems with clarification of the key factors limiting the yield gap in this region.
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MacCarthy, Dilys S., Myriam Adam, Bright S. Freduah, Benedicta Yayra Fosu-Mensah, Peter A. Y. Ampim, Mouhamed Ly, Pierre S. Traore, and Samuel G. K. Adiku. "Climate Change Impact and Variability on Cereal Productivity among Smallholder Farmers under Future Production Systems in West Africa." Sustainability 13, no. 9 (May 6, 2021): 5191. http://dx.doi.org/10.3390/su13095191.
Full textAbstract:
Agriculture in West Africa is constrained by several yield-limiting factors, such as poor soil fertility, erratic rainfall distributions and low input systems. Projected changes in climate, thus, pose a threat since crop production is mainly rain-fed. The impact of climate change and its variation on the productivity of cereals in smallholder settings under future production systems in Navrongo, Ghana and Nioro du Rip, Senegal was assessed in this study. Data on management practices obtained from household surveys and projected agricultural development pathways (through stakeholder engagements), soil data, weather data (historical: 1980–2009 and five General Circulation Models; mid-century time slice 2040–2069 for two Representative Concentration Pathways; 4.5 and 8.5) were used for the impact assessment, employing a crop simulation model. Ensemble maize yield changes under the sustainable agricultural development pathway (SDP) were −13 and −16%, while under the unsustainable development pathway (USDP), yield changes were −19 and −20% in Navrongo and Nioro du Rip, respectively. The impact on sorghum and millet were lower than that on maize. Variations in climate change impact among smallholders were high with relative standard deviations (RSD) of between 14% and 60% across the cereals with variability being higher under the USDP, except for millet. Agricultural production systems with higher intensification but with less emphasis on soil conservation (USDP) will be more negatively impacted by climate change compared to relatively sustainable ones (SDP).
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Cichota, R., I. Vogeler, F. Y. Li, and J. Beautrais. "Deriving pasture growth patterns for Land Use Capability Classes in different regions of New Zealand." Proceedings of the New Zealand Grassland Association 76 (January 1, 2014): 203–10. http://dx.doi.org/10.33584/jnzg.2014.76.2945.
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Abstract Farm system models are increasingly being used to assess the implications of land use and practice changes on profitability and environmental impacts. Exploring implications beyond individual farms requires the linkage of such models to land resource information, which for pastoral systems includes forage supply. The New Zealand Land Resource Inventory (LRI) and associated Land Use Capability (LUC) database includes estimates of the potential stock carrying capacity across the country, which can be used to derive annual, but not seasonal, patterns of pasture growth. The Agricultural Production Systems Simulator (APSIM) was used, with generic soil profiles based on descriptions of LUC classes, to generate pasture growth curves (PGCs) in three regions of the country. The simulated pasture yields were similar to the estimates in the LRI spatial database, and varied with LUC Class within and across regions. The simulated PGCs also agreed well with measured data. The approach can be used to obtain spatially discrete estimates of seasonal pasture growth patterns across New Zealand, enabling investigation of land use and management changes at regional scales. Key words: APSIM modelling, pasture growth curve (PGC), year to year variability, farm system analysis
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Vogeler, Iris, Rogerio Cichota, and Josef Beautrais. "Linking Land Use Capability classes and APSIM to estimate pasture growth for regional land use planning." Soil Research 54, no. 1 (2016): 94. http://dx.doi.org/10.1071/sr15018.
Full textAbstract:
Investigation of land-use and management changes at regional scales require the linkage of farm-system models with land-resource information, which for pastoral systems includes forage supply. The New Zealand Land Resource Inventory (NZLRI) and associated Land Use Capability (LUC) database include estimates of the potential stock-carrying capacity across the country, which can be used to derive estimates of average annual pasture yields. Farm system models and decision support tools, however, require information on the seasonal patterns of pasture growth. To generate such pasture growth curves (PGCs), the Agricultural Production Systems Simulator (APSIM) was used, with generic soil profiles based on descriptions of LUC classes, to generate PGCs for three regions of New Zealand. Simulated annual pasture yields were similar to the estimates of annual potential pasture yield in the NZLRI spatial database, and they provided information on inter-annual variability. Simulated PGCs generally agreed well with measured long-term patterns of seasonal pasture growth. The approach can be used to obtain spatially discrete estimates of seasonal pasture growth patterns across New Zealand for use in farm system models and for assessing the impact of management practices and climate change on the regional sustainability.
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Mei, Song, Yifei Tong, Fengque Pei, Zhiyu Song, and Yifan Shao. "Research on High Precision Magnetic Positioning Technology Based on Facility Transport Platform." Actuators 12, no. 1 (December 27, 2022): 13. http://dx.doi.org/10.3390/act12010013.
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With the continuous development of economic globalization, the research demand for intelligent agricultural machinery equipment in modern agriculture is increasing. This paper, which aims at the positioning problem of mobile robots in agriculture production, proposes a low-cost magnetic positioning scheme for cement ground. First, the analytical magnetic field model of ground magnets was established. Then, by comparing the analytic computing results, simulation results, and measured values, the modified model of magnetic fields was built and the relevant impact factors were calculated. After that, acquisition devices were used to collect the ground magnetic field data for the establishment of a magnetic field matching algorithm. Finally, the result showed that the positioning displacement error was ±1 mm, and the positioning accuracy was higher than the conventional indoor positioning method, which solved the problem of the low indoor positioning accuracy of agriculture mobile robots and contributes to the efficient production and modernization of agricultural machinery equipment.
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Yascaribay, Geovanny, Mónica Huerta, Miguel Silva, and Roger Clotet. "Performance Evaluation of Communication Systems Used for Internet of Things in Agriculture." Agriculture 12, no. 6 (May 30, 2022): 786. http://dx.doi.org/10.3390/agriculture12060786.
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The rapid development of Internet of Things (IoT) technology has provided ample opportunity for the implementation of intelligent agricultural production. Such technology can be used to connect various types of agricultural devices, which can collect and send data to servers for analysis. These tools can help farmers optimize the production of their crops. However, one of the main problems that arises in agricultural areas is a lack of connectivity or poor connection quality. For these reasons, in this paper, we present a method that can be used for the performance evaluation of communication systems used in IoT for agriculture, considering metrics such as the packet delivery ratio, energy consumption, and packet collisions. To achieve this aim, we carry out an analysis of the main Low-Power Wide-Area Networks (LPWAN) protocols and their applicability, from which we conclude that those most suited to this context are Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN). After that, we analyze various simulation tools and select Omnet++ together with the Framework for LoRa (FLoRa) library as the best option. In the first stage of the simulations, the performances of LoRa and LoRaWAN are evaluated by comparing the average propagation under ideal conditions against moderate propagation losses, emulating a rural environment in the coastal region of Ecuador. In the second phase, metrics such as the package delivery ratio and energy consumption are evaluated by simulating communication between an increasing number of nodes and one or two gateways. The results show that using two gateways with the Adaptive Data Rate technique can actively increase the delivery ratio of the network while consuming the same amount of energy per node. Finally, a comparison is made between the results of the simulation scenario considered in this project and those of other research works, allowing for the validation of our analytical and simulation results.