Current knowledge and future research opportunities for modeling annual crop mixtures. A review
Noémie GaudioAbraham A. Escobar GutierrezPierre CasadebaigJochem B. EversFrédéric GérardGaëtan LouarnNathalie ColbachSebastian MunzMarie LaunayHélène MarrouRomain BarillotPhilippe HinsingerJacques-Éric BergezDidier CombesJean‐Louis DurandEla FrakLoïc PagèsChristophe PradalSébastien Saint‐JeanWopke Van der WerfÉric Justes
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Crop Rotation
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A crop rotation experiment was established in 1996/97 at three locations representing different soil types and climates. Three factors were tested: i) crop rotation with different proportions of N2-fixing crops, ii) with and without a catch crop, and iii) with and without animal manure. A green manure crop increased yields in the following cereal crops, but at the rotational level, total yields were larger in crop rotations without a green manure crop. There were positive effects of animal manure and catch crops on yield. However, except for the coarse sandy soil, the yield effects of catch crops and animal manure decreased over time when a grass-clover green manure was included in the rotation. It appeared that the buffering effect of clover can counteract the positive yield effects of manure application and catch crop use. This shows the importance of assessing long-term effects in the evaluation of crop management measures.
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Crop Simulation Models (CSM) are computerized representations of crop growth, development and yield, simulated through mathematical equations as functions of soil conditions, weather and management practices. The Crop simulation models like agricultural production system simulator can save time and resources better prediction accuracy is the most important point that should be considered in decision making process. Most models are not tested or poorly tested, and hence their usefulness in decision making process is unproven. Therefore, this paper Reviews the performance of the APSIM CSM simulation accuracy with respect to the simulation of the growth, development and yield of the selected crops. APSIM model is reliable crop simulation model in predicting development, Growth and yield of different crops in the semi arid tropics. Keywords: APSIM, CSM, Yield, Semi arid tropics
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Crop rotation planning, being an essential prerequisite for organic farming, involves determining the species and timing of crops on farmland to improve soil quality, crop yield, and resistance to pests and weeds. Pre-crop values and crop rotation matrices describe the effect of a crop on the next crop, mediated through the soil. The identification of these effects in traditional long-term field studies is resource intensive. Within this paper we present AI4CROPR, a method to identify pre-crop values and crop rotation matrices using Normalized Difference Vegetation Index (NDVI) data from remote sensing, clustering, and artificial intelligence. Our method uses 24.352 unique crop rotations prevailing on plots in Lower Austria from 2017 to 2021. We restricted the crop rotations to the 26 most used crop types, which represent about 95 % of the crops grown in the area. For each plot and year, we estimated yield potential using the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 data. AI4CROPR enables the data-driven estimation of pre-crop values and creation of crop rotation matrices for entire regions based on their specific conditions and without the need to manually survey individual farms or plots. Validation has shown that results of the data- and AI-driven AI4CROPR method overlap to a great extent with recommendations from literature (28.20 % of the measured pre-crop values are identical to literature recommendations, 51.60 % deviate by one degree, and 19.67 % deviate by two degrees) and are suitable to extend the work to further regions and integrate them in crop rotation decision support systems.
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Year to year variation in yield is an inherent risk associated with crop production and many growers rely on intensive mechanical or chemical inputs to preserve crop yield in the face of fluctuating environmental conditions. However, as interest grows in alternative crop management systems which depend less on external inputs, determining the degree to which management systems can impact the temporal yield variability will help the development of sustainable agroecosystems. This study assessed average crop yields and temporal yield variability over a 12‐yr period in four agricultural management systems that are part of a long‐term cropping systems experiment at the W.K. Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) site in southwestern Michigan. The four systems follow a corn ( Zea mays L.), soybean [ Glycine max (L.) Merr.], and winter wheat ( Triticum aestivum L.) 3‐yr rotation under conventional (CT), no‐till (NT), low‐input (LI), or organic (ORG) management, and each crop phase was present in the rotation four times from 1993 to 2004. Yields were measured each year and crop yield variability was estimated using the coefficient of variation calculated separately for each crop phase. Averaged over the study period, yields in the CT and NT systems were similar across all crop phases of the rotation and of higher magnitude than the LI system only in the winter wheat phase of the rotation. Compared to the other three management systems, yields in the ORG system were lower in the corn and winter wheat phases of the rotation. Yields in the soybean phase were similar across the four management systems. Temporal yield variability differed among management systems and rotation phases and was highest in the ORG system during the soybean (CV = 48%) and winter wheat (CV = 33%) phases of the rotation. Compared to the CT system, yield variability was 40% lower in the LI (corn phase), 33% lower in the NT (soybean phase) and similar in the NT (corn and winter wheat phases) systems. Results of this study suggest that yield and temporal yield variability under alternative management systems such as no‐till and low‐input can be comparable to that in conventional systems. However, temporal yield variability can be as high or higher in organic cropping systems without external inputs of manure or compost.
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With the development of system and computer sciences,crop growth simulation modeling was developed faster,and it is commonly accepted as a new research field in agricultural science at present.On the basis of summarizing the characteristics and classification of crop simulation technology,this paper discusses the studies on growth simulation models of wheat crops and application status in China,also it suggests the problems and future approaches in simulation models development.
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