Datasets:

asadullahdogarr
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adaption-agriintel-contextual-yield-reasoning-v1

crop stringclasses 55 values	yield float64 0 21.1k ⌀	enhanced_prompt stringlengths 132 1.27k	enhanced_completion stringlengths 90 16.8k	reasoning_trace stringlengths 1.28k 52.8k	K int64 20 50	N int64 50 150	P int64 15 55	area float64 0.5 50.8M	avg_humidity_percent float64 34.5 86.1	avg_temp_c float64 6.26 28.6	fertilizer float64 54.2 4.84B	original_context stringlengths 52 76	pH float64 5.5 8	pesticide float64 0.09 15.8M	production int64 0 6.33B	season stringclasses 6 values	state stringclasses 30 values	total_rainfall_mm float64 249 5.24k	year int64 2k 2.02k
Other Rabi pulses	0.514444	Analyze the following list of values labeled 'Other Rabi pulses'. Identify the nature of this data, check for formatting inconsistencies (such as missing decimal points or irregular spacing), and provide a cleaned, structured version of the numbers if necessary. Data: Other Rabi pulses 2180.0 214010.6 566.8 26.35 ...	# Agronomic Data Integrity Assessment: 'Other Rabi Pulses' Metric Normalization ## 1. Data Characterization and Nature Identification The dataset provided is labeled "Other Rabi pulses." In agronomic classification, 'Rabi' denotes crops sown during the winter season (typically October to December) and harvested in the...	I need to analyze this dataset labeled 'Other Rabi pulses'. Let's break down what I'm looking at. First, the nature of the data: 'Rabi' refers to the winter cropping season, so these are winter-sown leguminous crops harvested in spring. The numerical values almost certainly represent agricultural production metrics—lik...	32	67	26	2,180	71.21	26.35	214,010.6	2180.0 214010.6 566.8 26.35 1100.2 71.21 67 26 32 6.3	6.3	566.8	1,090	Rabi	Odisha	1,100.2	2,000
Ragi	0.694	Analyze the following nutritional data for Ragi and provide a structured summary of its key components: Ragi 10107.0 1460360.43 2728.89 25.56 1884.14 73.6 85 40 45 6.2	### Compositional Assay and Nutritional Analysis: Eleusine coracana (Finger Millet/Ragi) 1.0 Dataset Integrity and Parameter Registry The provided numerical sequence represents raw analytical outputs derived from compositional assay protocols. To maintain data fidelity and adhere to strict hallucination filters,...	I need to parse this raw numerical sequence for Ragi (Eleusine coracana). The prompt provides ten data points without units or labels, so I must map them based on my deep knowledge of finger millet nutritional composition and agronomic metrics. Let me look at the sequence: 1. 10107.0 2. 1460360.43 3. 2728.89 4. 25.56 ...	45	85	40	10,107	73.6	25.56	1,460,360.43	10107.0 1460360.43 2728.89 25.56 1884.14 73.6 85 40 45 6.2	6.2	2,728.89	11,068	Rabi	West Bengal	1,884.14	2,013
Small millets	0.871429	Analyze the following dataset regarding 'Small millets'. The data consists of a label followed by a sequence of numerical values. Please interpret these figures, identifying potential metrics (such as production area, yield, or quantity) where possible, and present a structured summary or analysis of the provided infor...	# AGRONOMIC DATA INTERPRETATION REPORT: SMALL MILLETS CULTIVATION METRICS ## 1. Dataset Integrity and Metric Classification The provided heterogeneous dataset lacks explicit unit headers, necessitating a probabilistic classification based on agronomic magnitude standards for Panicum sumatrense and *Paspalum scrobicu...	I need to analyze this unlabeled dataset for 'Small millets'. The data contains ten numerical values: 2823.0, 278912.4, 818.67, 17.84, 2370.96, 79.73, 52, 16, 33, and 5.6. My objective is to probabilistically map these to standard agronomic metrics, construct a logical chain of how they interact to influence yield, and...	33	52	16	2,823	79.73	17.84	278,912.4	2823.0 278912.4 818.67 17.84 2370.96 79.73 52 16 33 5.6	5.6	818.67	2,282	Kharif	Meghalaya	2,370.96	1,998
Horse-gram	0.293103	Analyze the following dataset for 'Horse-gram' and provide a structured summary of the values, identifying potential metrics (e.g., production, price, area) based on the numerical sequence: Horse-gram 87826.0 8693017.48 21078.24 26.57 1857.72 73.53 67 26 32 6.3	# AGRONOMIC DATA INTERPRETATION PROTOCOL: HORSE-GRAM (Macrotyloma uniflorum) ## 1.0 Data Ingestion & Metric Classification The following numerical sequence has been ingested and mapped to standard agronomic and econometric parameters based on magnitude analysis, physiological thresholds for Macrotyloma uniflorum, ...	The user is requesting an analysis of a raw numerical dataset for 'Horse-gram'. I need to map these 10 seemingly disparate values to standard agronomic and econometric metrics. Let's look at the sequence: 87826.0, 8693017.48, 21078.24, 26.57, 1857.72, 73.53, 67, 26, 32, 6.3. First, I must classify these values based o...	32	67	26	87,826	73.53	26.57	8,693,017.48	87826.0 8693017.48 21078.24 26.57 1857.72 73.53 67 26 32 6.3	6.3	21,078.24	24,752	Kharif	Odisha	1,857.72	2,003
Rapeseed &Mustard	0.46	Analyze the following raw data regarding Rapeseed and Mustard statistics. The data includes production figures, area, yield, and other relevant metrics presented as a list of values. Please interpret these numbers, identify what each likely represents based on standard agricultural reporting contexts (e.g., production ...	### Agronomic Data Interpretation and Structured Analysis: Brassica Complex The provided raw numerical sequence constitutes a heterogeneous dataset of agronomic, edaphic, and economic indicators for the Rapeseed (Brassica napus) and Mustard (Brassica juncea) complex. The following deductive logic classifies thes...	I need to interpret this raw sequence of numbers for Rapeseed & Mustard. Let me sort these values by magnitude and apply standard agronomic reporting conventions to deduce what each represents. The values are: 8945.0, 1348906.0, 2772.95, 21.07, 878.81, 48.09, 60, 20, 40, 6.0. Starting with the largest value: 1348906....	40	60	20	8,945	48.09	21.07	1,348,906	8945.0 1348906.0 2772.95 21.07 878.81 48.09 60 20 40 6.0	6	2,772.95	3,969	Rabi	Himachal Pradesh	878.81	2,012
Sunflower	0.382609	Analyze the following dataset associated with the entity "Sunflower". The data consists of a label followed by a series of numerical values: Sunflower 430676.0 42550788.8 124896.04 23.73 1185.08 71.65 72 42 25 6.9 Please interpret these figures, identify potential patterns or anomalies, and provide a concise...	### Agronomic Data Interpretation and Yield Modeling Report: Entity Helianthus annuus (Sunflower) #### I. Variable Mapping and Parameter Classification The provided dataset consists of ten discrete numerical vectors. Based on magnitude analysis, biological plausibility, and standard agronomic parameter ranges for su...	I need to analyze this dataset for Helianthus annuus (sunflower). Let me break down these ten numerical values methodically, mapping them to standard agronomic metrics based on magnitude and biological plausibility. Starting with the first three values: 430,676.0, 42,550,788.8, and 124,896.04. These are large number...	25	72	42	430,676	71.65	23.73	42,550,788.8	430676.0 42550788.8 124896.04 23.73 1185.08 71.65 72 42 25 6.9	6.9	124,896.04	127,090	Kharif	Karnataka	1,185.08	1,998
Potato	13.186875	Analyze the following data entry starting with the identifier "Potato" and the associated numerical values. Interpret the likely meaning of this dataset, identify any patterns or anomalies in the numbers, and provide a concise summary of what this data might represent. Data: Potato 1555.0 252221.0 544.25 9.45 814....	### Agronomic Data Interpretation and Yield Modeling Report Dataset Identifier: Potato Raw Numerical Vector: [1555.0, 252221.0, 544.25, 9.45, 814.65, 55.72, 70, 25, 42, 6.7] --- #### I. Schema Inference and Variable Attribution In the absence of explicit metadata, variable mapping is deduced via magnitude ...	I need to interpret this raw numerical dataset associated with "Potato." As an agronomist, my system mandates evaluating soil chemistry (N, P, K, pH), regional climatic variables (rainfall, temperature, humidity), and seasonal markers to model crop yields. The prompt gives me 10 numbers: 1555.0, 252221.0, 544.25, 9.45,...	42	70	25	1,555	55.72	9.45	252,221	1555.0 252221.0 544.25 9.45 814.65 55.72 70 25 42 6.7	6.7	544.25	18,663	Kharif	Jammu And Kashmir	814.65	2,018
Ginger	16.008889	Analyze the following list of numerical data points and provide a statistical summary including the count, sum, average, minimum, and maximum values: Ginger 416.0 39382.72 104.0 28.13 1046.29 71.59 80 38 30 6.6	### Analytical Derivation and Variable Impact Assessment: Zingiber officinale (Ginger) Input Vector: $[416.0, 39382.72, 104.0, 28.13, 1046.29, 71.59, 80, 38, 30, 6.6]$ #### I. Statistical Summary The following descriptive statistics have been computed from the provided numerical dataset: \| Statistical Paramete...	I need to process this prompt carefully. I am acting as an elite AI Agronomist Operating Core, but I'm also constrained to act as a senior data analyst with 15 years of experience in agricultural supply chain metrics. I cannot explicitly mention my persona or background, but it must inform the quality and depth of my r...	30	80	38	416	71.59	28.13	39,382.72	416.0 39382.72 104.0 28.13 1046.29 71.59 80 38 30 6.6	6.6	104	6,665	Whole Year	Tamil Nadu	1,046.29	2,002
Urad	0.566667	Analyze the following dataset containing a label and a series of numerical values. Identify the context if possible, calculate basic statistical measures (such as sum, average, minimum, and maximum) for the numerical entries, and present the findings clearly. Data: Urad 60254.0 6394757.02 16268.58 25.79 1737.63 7...	### Contextual Identification and Data Stream Classification The primary label "Urad" identifies the dataset as pertaining to Vigna mungo (black gram), a leguminous crop critical to rotational agriculture systems. The accompanying numerical stream represents heterogeneous agronomic variables, encompassing production...	### Contextual Identification and Data Stream Classification The primary label "Urad" identifies the dataset as pertaining to Vigna mungo (black gram), a leguminous crop critical to rotational agriculture systems. The accompanying numerical stream represents heterogeneous agronomic variables, encompassing production...	45	85	40	60,254	72.95	25.79	6,394,757.02	60254.0 6394757.02 16268.58 25.79 1737.63 72.95 85 40 45 6.2	6.2	16,268.58	35,523	Kharif	West Bengal	1,737.63	1,999
Tobacco	5.57	Analyze the following raw data entry regarding Tobacco. The input consists of a category label followed by a sequence of numerical values. Please interpret these figures, identifying likely metrics (such as volume, revenue, percentages, or counts) based on their magnitude and context, and present a structured summary o...	### Commodity Data Analysis: Tobacco Sector Metrics The provided dataset consists of ten discrete numerical values associated with the "Tobacco" category. Based on magnitude, distribution, and standard commodity/agronomic reporting structures, these figures are interpreted as a composite of production volume, fiscal v...	I need to analyze this raw data sequence for Tobacco. The prompt requires me to act as an edge-optimized AI Agronomist but also as a senior data analyst specializing in commodity market metrics. The synthesis of these two profiles means I must interpret these numbers through both a market/pricing lens and an agronomic/...	50	65	28	44	81.79	26.23	4,669.72	44.0 4669.72 11.88 26.23 1758.79 81.79 65 28 50 5.7	5.7	11.88	245	Whole Year	Kerala	1,758.79	1,999
Sunflower	0.472632	Analyze the following dataset associated with "Sunflower" and provide a structured summary or interpretation of the values: Sunflower 2471.0 390195.61 815.43 26.21 1145.0 61.26 70 35 20 6.5	### Dataset Parameterization and Structural Mapping The provided heterogeneous data stream consists of ten discrete numerical values associated with Helianthus annuus (Sunflower) production. Based on magnitude analysis and standard agronomic ranges for precision agriculture modeling, the values are categorized into ...	Alright, let's break down this data stream for Helianthus annuus. I've got ten discrete numerical values to map to standard agronomic parameters. I need to figure out what each number represents based on typical magnitudes for sunflower production, then build a logical chain connecting these variables to the final yi...	20	70	35	2,471	61.26	26.21	390,195.61	2471.0 390195.61 815.43 26.21 1145.0 61.26 70 35 20 6.5	6.5	815.43	808	Kharif	Chhattisgarh	1,145	2,015
Coconut	13,003.78778	Analyze the following dataset labeled 'Coconut' and provide a statistical summary including total count, sum, mean, median, minimum, and maximum values. Data: Coconut 24273.0 2310061.41 7524.63 25.85 1564.19 72.28 85 40 45 6.2	# Statistical Agronomic Assessment: Coconut Dataset ## 1. Descriptive Statistical Summary The following metrics represent the central tendency and dispersion parameters derived from the provided heterogeneous data vector labeled 'Coconut'. All numerical outputs are strictly aligned with raw source records to maintain...	# Statistical Agronomic Assessment: Coconut Dataset ## 1. Descriptive Statistical Summary The following metrics represent the central tendency and dispersion parameters derived from the provided heterogeneous data vector labeled 'Coconut'. All numerical outputs are strictly aligned with raw source records to maintain...	45	85	40	24,273	72.28	25.85	2,310,061.41	24273.0 2310061.41 7524.63 25.85 1564.19 72.28 85 40 45 6.2	6.2	7,524.63	306,202,300	Whole Year	West Bengal	1,564.19	1,997
Ragi	0.833333	Please analyze the following dataset for the crop 'Ragi' and provide a structured summary or interpretation of these values: Ragi 119.0 17945.2 36.89 26.55 1398.23 70.87 67 26 32 6.3	### Agronomic Analysis Report: Eleusine coracana (Ragi) Dataset Parameter Mapping The provided heterogeneous data vector is mapped to standard agronomic and climatic variables based on magnitude alignment with established phenological and biochemical benchmarks for millet cultivation: * Nitrogen (N): 119....	I need to parse this raw data vector for Ragi and map it to standard agronomic parameters. Let me look at the values: 119.0, 17945.2, 36.89, 26.55, 1398.23, 70.87, 67, 26, 32, 6.3. First, I must identify the crop: Ragi, also known as finger millet (Eleusine coracana). It's a hardy millet, a C4 pathway plant, known f...	32	67	26	119	70.87	26.55	17,945.2	119.0 17945.2 36.89 26.55 1398.23 70.87 67 26 32 6.3	6.3	36.89	94	Summer	Odisha	1,398.23	2,012
Turmeric	6.2215	Analyze the following data entry for 'Turmeric' and interpret the associated numerical values: Turmeric 19339.0 3044732.16 7348.82 23.74 1088.08 68.27 72 42 25 6.9 Please identify the likely meaning of each number based on common agricultural or commodity data standards (e.g., production volume, area harvest...	### Agronomic Data Interpretation: Curcuma longa (Turmeric) Production Profile The following analysis decodes the provided numerical dataset associated with Turmeric (Curcuma longa) cultivation. The values are categorized into production metrics, climatic variables, and edaphic chemistry profiles based on magnitud...	I need to decode this sequence of numerical values for Turmeric. Let me approach this systematically by analyzing the magnitude and characteristics of each number, cross-referencing with standard agronomic reporting conventions for Curcuma longa. The sequence is: 1. 19339.0 2. 3044732.16 3. 7348.82 4. 23.74 5. 1088....	25	72	42	19,339	68.27	23.74	3,044,732.16	19339.0 3044732.16 7348.82 23.74 1088.08 68.27 72 42 25 6.9	6.9	7,348.82	122,764	Whole Year	Karnataka	1,088.08	2,017

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This dataset is a remastered version prepared using Adaption's Adaptive Data platform.

adaption-AgriIntel-Contextual-Yield-Reasoning-v1

A deeply evolved, instruction-tuned agricultural dataset engineered for precision agronomy modeling. This dataset bridges the gap between raw regional telemetry and generative AI reasoning by synthesizing historical crop performance with static soil chemistry metrics (N, P, K, pH) and historical macro-climatic atmospheric data (annual rainfall, temperature, humidity). Utilizing Adaption's synthesis pipeline, the raw data columns have been expanded into detailed, natural-language prompts paired with multi-step agronomic reasoning traces to prevent catastrophic forgetting and optimize fine-tuning accuracy for edge-deployed models.