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The pulse crop lentil (Lens culinaris Medik.) is often grown in crop rotations to provide nitrogen (N) and water benefits for subsequent crops. Lentil yields vary greatly with environmental factors and management. A reliable crop model for lentil could assist efforts to assess the effects of management practices to mitigate environmental stresses and maximize lentil yields. However, few crop models simulate the development and growth of lentil. In this study, we adapted the CSM-CROPGRO model in the Decision Support System for Agrotechnology Transfer to simulate lentil development and growth based on data collected from six experiments conducted from 2001 to 2021 globally. The initial parameter values taken from faba bean (Vicia faba L.) were modified based on reported information and analysis of observed data. Those values were fine-tuned to minimize the gaps between the simulated and observed crop attributes. The model simulated well development stages with root mean square error (RMSE) of 4 days and aboveground biomass with normalized root mean square error (nRMSE ≤ 23%). Seed yields were generally well simulated across experiments in calibration and validation (nRMSE = 19%) datasets, except for overestimation under the humid environment of Quebec in Canada, which may have resulted from excessive vegetative growth. The underlying mechanisms leading to excessive vegetative growth need to be explored further and included in the model for evaluating the adaptability of lentils to specific regions. Overall, the CSM-CROPGRO-Lentil model is ready for simulating lentil production under various scenarios, which may identify ways to improve the productivity and resiliency of cropping systems that include lentil. [ABSTRACT FROM AUTHOR]

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Introduction: Maralfalfa grass (Pennisetum spp.) is known for its high dry matter productivity and nutritive value. However, information on agronomic management practices to improve yield and nutritive value in Ethiopia is sparse. Materials and method: The experiment consisted of 7 plant densities (33,333 [75 cm × 40 cm]; 26,667[75 cm × 50 cm]; 25,000 [100 cm × 40 cm]; 22,222 [75 cm × 60 cm]; 20,000 [100 cm × 50 cm]; 16,667 [100 cm × 60 cm]; and 13,333 [125 cm × 60 cm] plants per hectare, and arranged in randomized complete block design with three replications. Results: The results indicated that the year had a significant (P<0.001) effect on measured traits. Plant densities had no significant (P>0.05) effect on the number of nodes and leaves per plant, leaf length, and basal diameters. The number of tillers per plant varied significantly among plant densities only in the first year at the 1st harvest. Plant height in the first year was significantly (P<0.01) greater than in the second year. Discussion: Annual dry matter production and annual crude protein production of Maralfalfa grass were not significantly affected by plant density. Nutritive value parameters (CP, Ash, NDF, ADF, ADL, and IVDMD) were not significantly (P > 0.05) influenced by plant density. Using a lower plant density could reduce the amount of planting material, transport, and labor costs. Conclusion: However, further studies on Maralfalfa grass should be conducted in multi-locations of Ethiopia both under rain-fed and irrigated conditions with various agronomic practices. [ABSTRACT FROM AUTHOR]