Ahmed K, Wang G, Yu M, Koo J, You, L (2015). Potential impact of Climate Change on cereal crop yield in West Africa. Climatic Change 133(2):321-334.
Crossref

Al‐Mahasneh M, Alkoaik F, Khalil A, Al‐Mahasneh A, El‐Waziry A, Fulleros R, Rababah T (2014). A generic method for determining moisture sorption isotherms of cereal grains and legumes using artificial neural networks. J. Food Process. Eng. 37(3):308-316.
Crossref

Arnell N (2016). The global-scale impacts of Climate Change: the QUEST-GSI project. Clim. Change 134(3):343-352.
Crossref

Barrero O, Rojas D, González C, Perdomo S (2016). Weed detection in rice fields using aerial images and neural networks. In Signal Processing, Images and Artificial Vision (STSIVA), Symposium. IEEE 21:1-4.
Crossref

Basche A, Archontoulis S, Kaspar T, Jaynes D, Parkin T, Miguez F (2016). Simulating long-term impacts of cover crops and Climate Change on crop production and environmental outcomes in the Midwestern United States. Agric. Ecosyst. Environ. 218:95-106.
Crossref

Beigi M, Harchegani M, Eshkaftaki M (2016). Prediction of paddy drying kinetics: a comparative study between mathematical and artificial neural network modeling. Chem. Ind. Chem. Eng. Q. pp. 39-39.

Bernal J, Díaz E, Méndez J, Cortés C, Boshell F, Mejías P, Stedutto P. (2013). Uso del modelo AquaCrop para estimar rendimientos para el cultivo de maíz en los departamentos de Córdoba, Meta, Tolima y Valle del Cauca. FAO Colombia, Bogotá P 161.

Bogard M, Ravel C, Paux E, Bordes J, Balfourier F, Chapman S, Allard V (2014). Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model. J. Exp. Bot. P. eru328.
Crossref

Bose P, Kasabov N, Bruzzone L, Hartono R (2016). Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series. IEEE Trans. Geosci. Remote Sens. 54(11):6563-6573.
Crossref

Boutraa T, Akhkha A, Al-Shoaibi A (2015). Evaluation of growth and gas exchange rates of two local saudi wheat cultivars grown under heat stress conditions. Pak. J. Bot. 47(1):27-34.

Chen Y, Zhang Z, Tao F, Palosuo T, Rötter R (2017). Impacts of heat stress on leaf area index and growth duration of winter wheat in the North China Plain. Field Crops Res. In Press. 
Crossref

Chourghal N, Lhomme J, Huard F, Aidaoui A (2016). Climate Change in Algeria and its impact on durum wheat. Reg. Environ. Change 16(6):1623-1634.
Crossref

Conde C (2014). Cambio climático y agrobiodiversidad. Rev. Colomb. Investig. Agroind. 1(1):72-79.
Crossref

Constantinescu A (2017). Neuro-fuzzy concepts applied for planning of the cereal crops: applications to the maize hybrids growing in a Romanian region. J. Biol. Dyn. 11(1):1-7.
Crossref

Covarrubias M, Bustamante W, Delgado C, Ibarra E (2014). Análisis de cuatro variables del período de lluvias asociadas al cultivo maíz de temporal. Rev. Mex. Cienc. Agríc. 5(1):101-114.

Crimp S, Zheng B, Khimashia N, Gobbett, D, Chapman S, Howden M, Nicholls N (2016). Recent changes in southern Australian frost occurrence: implications for wheat production risk. Crop Pasture Sci. 67(8):801-811
Crossref

Donné S, Luong Q, Goossens B, Dhondt S, Wuyts N, Inzé D, Philips W (2016). Machine learning for maize plant segmentation. In Belgian-

Eitzinger J, Thaler S, Schmid E, Strauss F, Ferrise R, Moriondo M, Olesen J (2013). Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. J. Agric. Sci. 151(06):813-835.
Crossref

Farjam A, Omid M, Akram A, Niari Z (2014). A neural network based modeling and sensitivity analysis of energy inputs for predicting seed and grain corn yields. J. Agric. Sci. Technol. 16(4):767-778.

Food and Agriculture Organization (FAO) (2016). El estado mundial de la agricultura y alimentación. Cambio climático, agricultura y seguridad alimentaria. 

View

Frederiks T, Christopher J, Sutherland M, Borrell A (2015). Post-head-emergence frost in wheat and barley: defining the problem, assessing the damage, and identifying resistance. J. Exper. Bot. 66(12):3487-3498.
Crossref

Gallo A (2015). Assessment of the Climate Change impact and adaptation strategies on Italian cereal production using high resolution climate data. Climatic Change 132:321-336.

Gandhi N, Petkar O, Armstrong L (2016). Rice crop yield prediction using artificial neural networks. In Technological Innovations in ICT for Agriculture and Rural Development (TIAR), 2016 IEEE (pp. 105-110). IEEE.
Crossref

García A, Del Fabro L (2015). Experience on food purchase from family farming by Brazil´ s School Lunch Program: design determining factors. Investigación Agraria 17(1):3-17.

Giroux M, Bourgeois G, Dion Y, Rioux S, Pageau D, Zoghlami S, Vanasse A (2016). Evaluation of forecasting models for Fusarium head blight of wheat under growing conditions of Quebec, Canada. Plant Dis. 100(6):1192-1201.
Crossref

Goyal S (2013). Predicting properties of cereals using artificial neural networks: A review. Sci. J. Crop Sci. 2(7):95-115.

Graß R, Thies B, Kersebaum K, Wachendorf M (2015). Simulating dry matter yield of two cropping systems with the simulation model HERMES to evaluate impact of future Climate Change. Eur. J. Agron. 70:1-10.
Crossref

He Z, Xia X, Peng S, Lumpkin T (2014). Meeting demands for increased cereal production in China. J. Cereal Sci. 59(3):235-244.
Crossref

Higuera A, Monroy D (2014). Variabilidad climática de la temperatura Impacto económico en el EBITDA de los cultivos de arroz riego mecanizado en la zona centro de Colombia para el periodo 1999-2013: medidas de adaptación. 

View

Ihsan M, El-Nakhlawy F, Ismail S, Fahad S (2016). Wheat phenological development and growth studies as affected by drought and late season high temperature stress under arid environment. Front. Plant Sci. P 7.
Crossref

Iizumi T, Ramankutty N (2016). Changes in yield variability of major crops for 1981–2010 explained by Climate Change. Environ. Res. Lett. 11(3):034003.
Crossref

Ji H, Xiao L, Xia Y, Song H, Liu B, Tang L, Liu L (2017). Effects of jointing and booting low temperature stresses on grain yield and yield components in wheat. Agric. For. Meteorol. 243:33-42.
Crossref

Jones A, Phillips A (2016). Historic Food Production Shocks: Quantifying the Extremes. Sustainability 8(5):427.
Crossref

Khoshroo A, Arefi A, Masoumiasl A, Jowkar G (2014). Classification of wheat cultivars using image processing and artificial neural networks. Agric. Commun. 2:17-22.

Krimly T, Apfelbeck J, Huigen M, Dabbert S, Reichenau T, Lenz-Wiedemann V, Schneider K (2016). Effects of Future Climate Changes on Yields, Land Use and Agricultural Incomes. In Regional Assessment of Global Change Impacts. Springer International Publishing pp. 609-614.
Crossref

Lal S, Varma S (2014). Identification of abiotic stress related cereal proteins based on their structural composition using back propagation networks. J. Agroecol. Nat. Resour. Manage. 1(4):270-274.

Lv H, Lei T, Huang X, Zhang Y (2015). Application of an Improved Grey Neural Network in Grain Yield Prediction.

Mansourian S, Darbandi E, Mohassel M, Rastgoo M, Kanouni H (2017). Comparison of artificial neural networks and logistic regression as potential methods for predicting weed populations on dryland chickpea and winter wheat fields of Kurdistan province, Iran. Crop Prot. 93:43-51.
Crossref

Mao X, Sun L, Hui G, Xu L (2014). Modeling research on wheat protein content measurement using near-infrared reflectance spectroscopy and optimized radial basis function neural network. J. Food Drug Anal. 22(2):230-235.
Crossref

Matsumura K, Gaitan C, Sugimoto K., Cannon A, Hsieh W (2015). Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. J. Agric. Sci. 153(03):399-410.
Crossref

Mora J, Hurtado D, Sandoval O (2016). Preliminary monitoring of physiopathies incidence using digital image processing. Biotecnol. Sector Agropecu. Agroind. 14(1):45-52.

Nelson G, Rosegrant M, Koo J, Robertson R, Sulser T, Zhu T, Magalhaes M (2009). Cambio Climático: El impacto en la agricultura y los costos de adaptación. IFPRI. 

View

Nu-ez E, Barchi A, Ito S, Escaramboni B, Herculano R, Mayer C, Neto P (2016). Artificial intelligence approach for high level production of amylase using Rhizopus microsporus var oligosporus and different agro‐industrial wastes. J. Chem. Technol. Biotechnol. 92(3):684-692.

Organisation for Economic Co-operation and Development/Food and Agriculture Organization (OECD/FAO) (2017). OECD-FAO Agricultural Outlook 2017-2026. Publishing, Paris. 

Pérez V, Omar F (2015). Efecto del cambio climático en el maíz y frijol de Honduras evidenciado a través de la matriz de contabilidad social. 

View

Perry E, Nuttall J, Wallace A, Fitzgerald G (2017). In-field methods for rapid detection of frost damage in Australian dryland wheat during the reproductive and grain-filling phase. Crop Pasture Sci. 68(6):516-526.
Crossref

Pimentel A, Rocha J, Souza M, Ribeiro G, Silva C, Oliveira I (2015). Characterization of heat tolerance in wheat cultivars and effects on production components. Rev. Ceres 62(2):191-198.
Crossref

Ravari S, Dehghani H, Naghavi H (2016). Assessment of salinity indices to identify Iranian wheat varieties using an artificial neural network. Ann. Appl. Biol. 168(2):185-194.
Crossref

Rawson H, Macpherson G (2001). Trigo regado: Manejo del cultivo. Food Agric. Org. 

View

Reeves T, Thomas G, Ramsay G (2016). Ahorrar para crecer en la práctica: maíz, arroz, trigo. Guía para la producción sostenible de cereales. 

View

Robles M, Hernández J, Ortega J, Martínez J, Castillo I, Sosa E, Torres A (2015). Obtención de híbridos de maíz para grano en la Comarca Lagunera y Aguascalientes, México. Ecosist. Recurs. Agropecu. 2(6):245-254.

Roncancio S, Mu-oz J, Sánchez F (2016). Policultivos y sistemas silvopastoriles. Estrategias para la adaptación al cambio climático. ISBN: 978-958-763-156-2 

Rose G, Osborne T, Greatrex H, Wheeler T (2016). Impact of progressive global warming on the global-scale yield of maize and soybean. Clim. Change 134(3):417-428.
Crossref

Sakamoto T, Gitelson A, Arkebauer T (2013). MODIS-based corn grain yield estimation model incorporating crop phenology information. Remote Sens. Environ. 131:215-231.
Crossref

Sanad M, Campbell K, Gill K (2016). Developmental program impacts phenological plasticity of spring wheat under drought. Bot. Stud. 57(1):35.
Crossref

Sapkota T, Jat M, Aryal J, Jat R, Khatri-Chhetri A (2015). Climate Change adaptation, greenhouse gas mitigation and economic profitability of conservation agriculture: Some examples from cereal systems of Indo-Gangetic Plains. J. Integr. Agric. 14(8):1524-1533.
Crossref

Serio L (2015). Desarrollo y validación de un modelo del sistema suelo-planta-atmósfera para la estimación de la evapotranspiración real del cultivo de maíz (Doctoral dissertation, Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires).

Sharifi H, Hijmans R, Hill J, Linquist B (2016). Using Stage-Dependent Temperature Parameters to Improve Phenological Model Prediction Accuracy in Rice Models. Crop Sci. 57(1):444-453.
Crossref

Shi W, Jiao K, Liang Y, Wang F (2016). Efficient detection of internal infestation in wheat based on biophotonics. J. Photochem. Photobiol. B: Biol. 155:137-143.
Crossref

Sosa R, Carballo A, Mir H, Livera A (2015). Calidad de la semilla de trigo de temporal en función del ambiente de producción. Rev. Mex. Cienc. Agríc. 6(6):1239-1251.

Tesfaye K, Gbegbelegbe S, Cairns J, Shiferaw B, Prasanna B, Sonder K, Robertson R (2015). Maize systems under Climate Change in sub-Saharan Africa: Potential impacts on production and food security. Int. J. Clim. Change Strat. Manage. 7(3):247-271.
Crossref

Torres I (2017). La crisis alimentaria Mundial: causas y perspectivas para su entendimiento|| World Food Crisis: Causes and Prospects for their Understanding. Razón Palabra 20(3-94):602-621.

Torres J, Landeros F, Enciso T, Sa-udo M (2012). Phenological prediction models for white corn (Zea mays L.) and fall armyworm (Spodoptera frugiperda JE Smith). Agrociencia (Montecillo) 46(4):399-410.

Trigo Y (2015). El centro de investigación y mejoramiento del maíz y el trigo (CIMMYT). Sociol. Méx. P 16.

Tripathi R, Kalyani V, Ram L, Jha S (2015). Prediction of Wheat Yield from Pond Ash Amended Field by Artificial Neural Networks. J. Hazard. Toxic Radioact. Waste 19(4):04015001.
Crossref

Trnka M, Hlavinka P, Semenov M (2015). Adaptation options for wheat in Europe will be limited by increased adverse weather events under Climate Change. J. Royal Soc. Interf. 12:112.
Crossref

Wallach D, Nissanka S, Karunaratne A, Weerakoon W, Thorburn P, Boote K, Jones J (2017). Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice. Eur. J. Agron. Elsevier 88:53-62.
Crossref

Wise T, Garvey E (2012). Mexican Rural Development Research Reports. 

View

Yang J, Gong W, Shi S, Du L, Sun J, Song S (201). Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice. Plant Soil Environ. 62:178-183.
Crossref

Zhao C, Liu B, Piao S, Wang X, Lobell D, Huang Y, Durand J, Elliott J, Ewert F, Janssens IA, Li T, Lin E, Liu Q, Martre P, Müller C, Peng S, Pe-uelas J, Ruane AC, Wallach D, Wang T, Wu D, Liu Z, Zhu Y, Zhu Z, Asseng S (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci. 114(35):9326-9331.
Crossref